August 11, 2025

Beyond Story Points: Rethinking Software Engineering Productivity in the Age of AI

Mark Phillips

Why traditional metrics fall short, and how modern frameworks like DORA and SPACE can guide better outcomes

For years, engineering leaders have relied on familiar metrics to gauge developer performance: story points, bug counts, and lines of code. These measures offered a shared baseline, especially in Agile environments where estimation and output needed a common language.

But in today’s AI-assisted world, those numbers no longer tell the full story. Performance isn’t just about volume or velocity—it’s about outcomes. Did the developer deliver the expected functionality, with the right quality, on time? That’s how we compensate today, and that’s still what matters. But how we measure those things must evolve.

With tools like GitHub Copilot, Claude Code, and Cursor generating entire functions, tests, and documentation quickly, output is becoming less about what a developer types and more about what they model, validate, and evolve.

The challenge for CIOs, CTOs, and SVPs of Engineering isn’t just adopting new tools. It’s rethinking how to measure effectiveness in a world where productivity is amplified by AI and complexity often hides behind automation.

Why Traditional Metrics Break Down

The future of measurement hinges on three categories: productivity, quality, and functionality. These have always been essential to evaluating engineering work. But in the AI era, we must measure them differently. That shift doesn’t mean abandoning objectivity; it means updating our tools.

Today’s AI-assisted workflows lack mature solutions for tracking whether functionality requirements—like EPICs and user stories—have been fully met. But new approaches, like multi-domain linking (MDL), are emerging to close that gap. Measurement is getting smarter, and more connected, because it has to.

The problem isn’t that legacy metrics are useless. It’s that they’re easily gamed, misinterpreted, or disconnected from business value.

Story points were never meant to be performance metrics. They were team-based estimates but are often misused to compare individuals.

Lines of code favor verbosity over impact. In an AI-powered world, writing less can mean doing more.

Velocity becomes misleading when AI inflates the number of completed tasks, even if they lack strategic value.

Quality, or the number of bugs found during testing, can reflect poor understanding of story points and not poor coding.

At best, these metrics create noise. At worst, they drive harmful incentives, like rewarding speed over safety, or activity over alignment.

The Future of Measurement: Productivity, Quality, Functionality

The Rise of Directional Metrics

Modern frameworks like DORA and SPACE were built to address these gaps.

DORA (DevOps Research and Assessment) focuses on:

Deployment frequency

Lead time for changes

Change failure rate

Mean time to restore service

These measure delivery health, not just effort. They’re useful for understanding how efficiently and safely value reaches users.

SPACE (developed by Microsoft Research) considers:

Satisfaction and well-being

Performance

Activity

Communication and collaboration

Efficiency and flow

SPACE offers a more holistic view, especially in cross-functional and AI-assisted teams. It acknowledges that psychological safety, cross-team communication, and real flow states often impact long-term output more than individual commits.

AI Complicates the Picture

AI tools don’t eliminate the need for metrics; they demand smarter ones. When an LLM can write 80% of the code for a feature, how do we credit the developer? By the number of keystrokes? Or by their judgment in prompting, curating, and validating what the tool produced?

But here’s the deeper challenge: What if that feature doesn’t do what it was supposed to?

In AI-assisted workflows:

Code volume no longer maps to effort

Execution time can be reduced, but review and validation time increases

Errors shift from logic bugs to alignment gaps—where delivered functionality doesn’t match requirements

Productivity isn’t just about output—it’s about fitness to purpose. Without strong traceability between code, tests, user stories, and epics, it’s easy for teams to ship fast but fall short of the business goal.

Many organizations today struggle to answer a basic question: Did this delivery actually fulfill the intended functionality?

This is where multi-domain linking (MDL) and AI-powered traceability show promise. By connecting user stories, requirements, test cases, design artifacts, and even user feedback within a unified graph, teams can use LLMs to assess whether the output truly matches the input.

And this capability unlocks more than just better alignment—it opens the door to innovation. AI-assisted development enables organizations to build more complex, interconnected, and adaptive systems than ever before. As those capabilities expand, so too must our ability to measure their economic value. What applications can we now build that we couldn’t before? And what is that worth to the business?

That’s not a theoretical exercise. It’s the next frontier in engineering measurement.

Productivity as a System, Not a Score

The best engineering organizations treat productivity like instrumentation. No single number can tell you what’s working, but the right mix of signals can guide better decisions. That system must account for both delivery efficiency and functional alignment. High velocity is meaningless if the outcome doesn’t meet the requirements it was designed to fulfill.

That means:

Creating dashboards that show patterns, not just totals

Blending technical metrics (DORA) with team dynamics (SPACE)

Tracking improvements over time, not absolutes per sprint

Using metrics as coaching tools, not judgment tools

Most importantly, it means aligning measurement to what matters: Did the product deliver value? Did it meet its intended function? Was the effort worth the outcome? Those are the questions that still define success—and the ones our measurement frameworks must help answer.

How to Start Rethinking Measurement

If your metrics haven’t evolved alongside your tooling, here’s how to get started:

Audit your current metric stack. What are you measuring, and what are you missing?

Align on outcomes. What does “good” look like for your business, not just your codebase?

Pick 2–3 directional metrics from DORA or SPACE that reflect your actual goals.

Baseline and benchmark. Don’t look for high scores; look for trends and improvement.

Build measurement into your retros. Turn metrics into prompts for discussion, not weapons for comparison.

AI is reshaping how software gets built. That doesn’t mean productivity can’t be measured—it means it must be measured differently. The leaders who shift from tracking motion to monitoring momentum will build faster, healthier, and more resilient engineering teams.

Robots & Pencils: Measuring What Matters in an AI-Driven World

At Robots & Pencils, we believe productivity isn’t a score—it’s a system. A system that must measure not just speed, but alignment. Did the output meet the requirements? Did it fulfill the epic? Was the intended functionality delivered?

We help clients extend traditional measurement approaches to fit an AI-first world. That means combining DORA and SPACE metrics with functional traceability—linking code to requirements, outcomes to epics, and user stories to business results.

Our secure, AWS-native platforms are already instrumented for this kind of visibility. And our teams are actively designing multi-domain models that give leaders better answers to the questions they care about most.

As AI opens the door to applications we never thought were possible, our job is to help you measure what matters—including what’s newly possible. We don’t just help teams move faster. We help them build with confidence—and prove it.

August 11, 2025

Pilot, Protect, Produce: A CIO’s Guide to Adopting AI Code Tools

Tyler Klein

How to responsibly explore tools like GitHub Copilot, Claude Code, and Cursor—without compromising privacy, security, or developer trust

AI-assisted development isn’t a future state. It’s already here. Tools like GitHub Copilot, Claude Code, and Cursor are transforming how software gets built, accelerating boilerplate, surfacing better patterns, and enabling developers to focus on architecture and logic over syntax and scaffolding.

The productivity upside is real. But so are the risks.

For CIOs, CTOs, and senior engineering leaders, the challenge isn’t whether to adopt these tools—it’s how. Because without the right strategy, what starts as a quick productivity gain can turn into a long-term governance problem.

Here’s how to think about piloting, protecting, and operationalizing AI code tools so you move fast, without breaking what matters.

Why This Matters Now

In a recent survey of more than 1,000 developers, 81% of engineers reported using AI assistance in some form, and 49% reported using AI-powered coding assistants daily. Adoption is happening organically, often before leadership even signs off. The longer organizations wait to establish usage policies, the more likely they are to lose visibility and control.

On the other hand, overly restrictive mandates risk boxing teams into tools that may not deliver the best results and limit experimentation that could surface new ways of working.

This isn’t just a tooling decision. It’s a cultural inflection point.

Understand the Risk Landscape

Before you scale any AI-assisted development program, it’s essential to map the risks:

Data leakage: Code snippets may contain proprietary logic or PII. With some tools, there’s a risk that these are logged, transmitted, or even used in model training.

Telemetry and usage tracking: Many tools send back usage metadata, which could raise compliance or IP concerns in regulated environments.

Model transparency: Enterprise IT teams often have limited visibility into how third-party LLMs are trained or updated.

Token costs: High-volume usage of external LLMs like Anthropic’s Claude or OpenAI’s GPT-4 can drive significant costs if left unmonitored.

These aren’t reasons to avoid adoption. But they are reasons to move intentionally with the right boundaries in place.

Protect First: Establish Clear Guardrails

Protect First: Establish Clear Guardrails

A successful AI coding tool rollout begins with protection, not just productivity. As developers begin experimenting with tools like Copilot, Claude, and Cursor, organizations must ensure that underlying architectures and usage policies are built for scale, compliance, and security.

Consider:

Private repo isolation: Restrict tool access to non-sensitive codebases or open-source contributions during pilot phases.

In-house proxies or middle layers: Route prompt traffic through approved gateways that monitor or sanitize inputs.

Enterprise contracts over consumer logins: Ensure tools used by developers are under organizational agreements with clear data handling terms.

LLM containment strategies: For high-sensitivity environments, explore containerized models or fully managed options through secure platforms like Amazon Bedrock. Bedrock enables teams to use leading foundation models, including Anthropic’s Claude, within an enterprise-grade boundary, with no risk of model training leakage.

For teams ready to push further, Bedrock AgentCore offers a secure, modular foundation for building scalable agents with memory, identity, sandboxed execution, and full observability—all inside AWS. Combined with S3 Vector Storage, which brings native embedding storage and cost-effective context management, these tools unlock a secure pathway to more advanced agentic systems.

Most importantly, create an internal AI use policy tailored to software development. It should define tool approval workflows, prompt hygiene best practices, acceptable use policies, and escalation procedures when unexpected behavior occurs.

These aren’t just technical recommendations, they’re prerequisites for building trust and control into your AI adoption journey.

Pilot Intentionally

Start with champion teams who can balance experimentation with critical evaluation. Identify low-risk use cases that reflect a variety of workflows: bug fixes, test generation, internal tooling, and documentation.

Track results across three dimensions:

Developer experience: Does the tool actually help, or does it create new friction?

Code quality: Are generated suggestions valid, performant, and secure?

Team patterns: How do developers prompt? What guardrails do they naturally adopt or ignore?

Encourage developers to contribute usage insights and prompt examples. This creates the foundation for internal education and tooling norms.

Don’t Just Test—Teach

AI coding tools don’t replace development skills; they shift where those skills are applied. Prompt engineering, semantic intent, and architectural awareness become more valuable than line-by-line syntax.

That means education can’t stop with the pilot. To operationalize safely:

Embed coaching into code reviews (e.g., flagging unsafe prompt usage)

Create internal wikis or LLM-safe prompt libraries

Train tech leads on where generation helps—and where it hurts

Build reusable workflows for common AI development scenarios

When used well, these tools amplify good developers. When used poorly, they obscure problems and inflate false productivity. Training is what makes the difference.

Produce with Confidence

Once you’ve piloted responsibly and educated your teams, you’re ready to operationalize with confidence. That means:

Defining tool selection criteria for different project types

Monitoring token usage and LLM cost impact

Establishing a feedback loop between engineering, IT, and security

Treating AI-assisted development as an evolving discipline—not a one-time rollout

Organizations that do this well won’t just accelerate development, they’ll build more resilient software teams. Teams that understand both what to build and how to orchestrate the right tools to do it. The best engineering leaders won’t mandate one AI tool or ban them altogether. They’ll create systems that empower teams to explore safely, evaluate critically, and build smarter together.

Robots & Pencils: Secure by Design, Built to Scale

At Robots & Pencils, we help enterprise engineering teams pilot AI-assisted development with the right mix of speed, structure, and security. Our preferred LLM, Anthropic, was chosen precisely because we prioritize data privacy, source integrity, and ethical model design—values we know matter to our clients as much as productivity gains.

We’ve been building secure, AWS-native solutions for over a decade, earning recognition as an AWS Partner with a Qualified Software distinction. That means we meet AWS’s highest standards for reliability, security, and operational excellence while helping clients adopt tools like Copilot, Claude Code, and Cursor safely and strategically.

We don’t just plug in AI; we help you govern it, contain it, and make it work in your world. From guardrails to guidance, we bring the technical and organizational design to ensure your AI tooling journey delivers impact without compromise.

August 11, 2025

The Changing Role of the Computer Programmer

Nicholas Waynik

How generative AI, cloud-native services, and intelligent orchestration are redefining the developer role—and what it means for modern engineering teams

In the early days of computing, programmers were indispensable because they were the only ones who could speak the language of machines. From punch cards to assembly language, software development was hands-on and highly specialized. Even as languages evolved, from COBOL and C to Java and C#, one thing stayed constant: developers wrote every line themselves.

But that’s no longer true. And it hasn’t been for a while.

Today, enterprise developers have access to an entirely new class of tools: generative AI, intelligent agents, and secure, cloud-native building blocks that reduce the need to write, or even see, large amounts of code. This shift isn’t superficial. It’s redefining the nature of software development itself.

A recent Cornell University study reports that AI now generates at least 30% of Python code in major repositories in the U.S. And in enterprise environments at Google and Microsoft, 30–40% of new code is reported as AI-generated. That’s not a tweak in tooling. That’s a turning point in how software gets built.

From Code to Composition

For decades, the dominant paradigm in programming was one of writing: the developer’s job was to build logic from scratch, test it for accuracy, and ensure it could scale. As complexity grew, so did the stack of tools—IDEs, frameworks, QA platforms, and versioning systems—to support that work.

But in the last few years, the developer toolbox has changed dramatically. Tools like GitHub Copilot, Claude Code, and Cursor now generate reliable code in real time. Entire modules can be scaffolded with a few prompts. Meanwhile, cloud platforms like AWS offer modular services that handle everything from authentication to observability out of the box.

The result? Developers are shifting from authors to orchestrators. The value isn’t in how much code they can write; it’s in how well they can assemble, adapt, and govern systems that are increasingly AI-enabled, cloud-native, and composable.

Productivity and Quality are Improving, but are We Building the Right Thing?

AI-assisted development produces measurable gains. Code is being written faster. Boilerplate is disappearing. Bugs are easier to catch early. Even tests can be autogenerated. And yet, one challenge persists: verifying that the right thing is being built.

It’s relatively straightforward to measure productivity (lines of code, lead time) and quality (bug rates, test coverage). But ensuring correct functionality—matching what’s shipped to product requirements, user stories, and EPICs—is harder than ever. Code generation tools accelerate output, but they don’t always ensure alignment with intent.

That’s why the developer’s role is expanding. Understanding product vision, aligning technical architecture with business goals, and managing evolving requirements are becoming just as critical as technical skill.

What Should Engineering Leaders Expect from Modern Developers?

The pace of innovation in AI development tools is relentless. What a developer learns today may be outdated in a few months. This puts enormous pressure on engineering leaders to balance experimentation with sustainability.

The safest path forward? Anchor learning and experimentation within robust cloud ecosystems. AWS, for instance, offers stable development trajectories, strong security guardrails, and continuous improvements that minimize disruption. The goal isn’t to chase every new tool; it’s to build foundational fluency and adapt deliberately.

To succeed in this new environment, developers must think differently:

Less about how to write a function, more about which system can best provide it

Less about syntax, more about semantics

Less about build velocity, more about outcome orchestration

Code Isn’t Dead, but It’s Being Delegated

Let’s be clear: programming isn’t going away. But its role is evolving. The most impactful developers won’t be those who write the most lines of code, they’ll be the ones who know how to compose, configure, and coordinate intelligent systems with speed and confidence.

They’ll use prompts, ontologies, and models as naturally as they once used loops and conditionals. They’ll know when to generate, when to review, and when to intervene. And they’ll be deeply embedded in outcome-oriented thinking.

What Should Engineering Leaders Do Next?

As the role of the programmer changes, so too must the systems that support them. This means:

Reexamining how engineering productivity is measured

Establishing responsible adoption frameworks for AI code generation

Investing in training and team design that prioritizes orchestration, not just execution

The ground is shifting. But for organizations willing to embrace this change, the opportunity is enormous: faster iteration, stronger alignment, and more resilient systems—built by developers who think in outcomes, not just code.

Robots & Pencils: Redefining the Role, Rebuilding the Foundation

At Robots & Pencils, we’ve spent over a decade helping organizations adapt to shifts in software architecture and engineering practice. As developers move from coding line-by-line to orchestrating intelligent, cloud-native systems, our role is to help them—and their leaders—make that leap with confidence.

We design secure, cloud-native environments that empower developers to compose, not just code. With Anthropic as our preferred LLM and a track record of building modular, scalable solutions, we give teams the foundation they need to experiment responsibly, build faster, and deliver more value without compromising on security or quality.

For teams rethinking what it means to “write software,” we bring the expertise, architecture, and systems design to make the next role of the developer a strength, not a risk.

August 4, 2025

Patrick Higgins Named Chief Revenue Officer at Robots & Pencils

Robots & Pencils

From IBM transformation to AI-powered product strategy, Higgins brings proven enterprise expertise and client-first vision to fuel the firm’s next phase of commercial expansion

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native web, mobile, and app modernization, today announced the appointment of Patrick Higgins as Chief Revenue Officer (CRO). A seasoned technology leader with over 15 years of experience driving digital innovation for Fortune 500 companies, Higgins steps into a pivotal role to deepen client partnerships, scale impact, and fuel the company’s next phase of growth.

Higgins built his career at the intersection of digital product development, enterprise transformation, and applied AI. He began at IBM delivering mission-critical programs for large government and healthcare clients. Higgins then spent nearly a decade at WillowTree, where he helped scale the firm into a full-service digital agency and led go-to-market efforts across Media, Healthcare, and most recently, AI. Over the past year alone, he has advised more than 80 organizations on how to turn AI ambitions into action through strategic governance, rapid prototyping, and practical deployment strategies.

Now, as CRO at Robots & Pencils, Higgins will lead all commercial operations, with a focus on aligning strategy, sales, and client partnerships to help organizations unlock the full potential of AI, cloud-native architecture, and next-generation experiences.

“Patrick’s ability to listen deeply, build trust, and connect business goals to technical outcomes is exceptional,” said Leonard Pagon, CEO of Robots & Pencils. “He doesn’t just understand AI—he understands how to activate it inside the enterprise. He’s helped clients across industries turn emerging tech into scalable solutions, and his presence here marks a key step in our evolution. We’re not chasing growth for growth’s sake—we’re scaling the way we serve our clients. Patrick is the right leader to ensure that growth stays grounded in trust, results, and partnership.”

Higgins joins a growing executive team committed to challenging the traditional global systems integrators with a model that prioritizes speed, strategy, and elite delivery. With global centers of excellence and strategic partnerships with AWS, Salesforce, and Databricks, Robots & Pencils is positioned to help clients move beyond experimentation and into meaningful, AI-infused transformation.

“What drew me to Robots & Pencils is the caliber of the team and the clarity of the mission,” said Higgins. “We’re not just talking about AI. We’re delivering it—wrapped in thoughtful design, modern cloud infrastructure, and agile engineering. This is a firm built to move fast and deliver real results, and I’m honored to help lead the next chapter.”

In addition to his leadership role, Higgins is an active contributor to the AI community, serving as a panelist for the University of Virginia’s AI initiatives and helping organizations demystify their path to innovation. He holds a BA and MBA from the University of Virginia and lives in Charlottesville with his family.

August 4, 2025

Context Is King: How AWS & Anthropic Are Redefining AI Utility with MCP

Patrick Higgins

If AI is going to work at scale, it needs more than a model; it needs access, structure, and purpose.

At the AWS Summit in New York City, one phrase stuck with us:

“Models are only as good as the context they’re given.”

It came during an insightful joint session from AWS and Anthropic on Model Context Protocol (MCP), a deceptively simple concept with massive implications.

Across this recap series, we’ve explored the rise of agentic AI, the infrastructure required to support it, and the ecosystem AWS is building to accelerate adoption. MCP is the connective tissue that brings it all together. It’s how you move from smart models to useful systems.

Why Context Is the New Bottleneck

Generative AI has been evolving fast, but enterprise implementation is still slow. Why?

Because no matter how advanced your model is, it can’t help you make better decisions if it’s not connected to what makes your business unique: Your data. Your tools. Your systems. Your users.

That’s where MCP comes in.

What Is MCP—and Why It Matters

Model Context Protocol (MCP) is a specification that allows AI models to dynamically discover and interact with third-party tools, data sources, and instructions. Think of it as a structured interface—where systems publish a list of tools, what they do, the inputs they require, and how the model should use them.

For executives, that means your AI agents can tap into real business logic—not by guessing, but by calling documented resources your teams control. For engineers, it means you can expose functions, services, or datasets via an MCP server, enabling LLMs to perform meaningful actions without hardcoding every step.

The result? AI that doesn’t just respond—it executes, using tools it finds and understands in real time.

With MCP, you can:

Connect any model to any MCP-enabled system: Pull data from structured and unstructured sources, trigger tools, and interact with APIs—on demand.

Assign agent permissions and scopes: Let different agents access different tools based on business roles or workflows.

Enable intelligent task execution: Run lightweight services (“microtools”) directly from the MCP environment—no handoffs required.

Handle complex use cases: With built-in function calling, system prompting, sampling, registry, elicitation, and discovery, MCP lets agents ask questions, call other models, and complete multi-step tasks without breaking context.

In short: MCP allows generative AI to break free of the chat window and take real-world action.

Real Integration, Not Just Model Tuning

With MCP servers already available in AWS, your teams can start building agentic AI products that can utilize your unique business logic, customer data, and internal systems. This isn’t hypothetical. It’s real and ready to deploy today.

At Robots & Pencils, we’re already using this pattern with our clients:

Exposing legacy data through modern interfaces

Building modular tools for agents to call dynamically

Designing UX around co-pilots that ask users the right questions at the right moment

We call this approach Emergent Experience Design, a framework for building systems where agents adapt, interfaces evolve, and outcomes unfold through interaction. If you’re rethinking UX in the age of AI, this is where to start.

And when you combine this with what we covered in The Future Is Agentic, Modernization Reloaded, and From AI to Execution, you start to see the bigger picture: Agentic AI isn’t just a new model. It’s a new way of working. And context is the infrastructure it runs on.

Plug AI into the Business, Not Just the Cloud

The hype phase of generative AI is behind us. What matters now is how well your systems can support intelligent action. If you want AI that drives real outcomes, you don’t just need better models. You need better context. That’s the promise of MCP—and the opportunity ahead for organizations ready to take the next step.

If you’re experimenting with GenAI and want to connect it to your real-world data and systems, we should talk.

August 4, 2025

Nathan Carmon Named Chief Operating Officer of Robots & Pencils

Robots & Pencils

Seasoned transformation leader joins the executive team to scale AI-first growth, elevate client delivery, and drive operational excellence

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native web, mobile, and app modernization, today announced the appointment of Nathan Carmon as Chief Operating Officer (COO). A battle-tested operator and longtime business partner to CEO Leonard Pagon, Carmon joins at a pivotal moment to bring sharper execution, deepen client value, and accelerate the company’s AI-first momentum.

Carmon’s mandate isn’t just about growing fast—it’s about growing right. That means investing in people, refining delivery systems, and ensuring every client engagement is marked by clarity, care, and measurable impact. His arrival marks another major milestone in Robots & Pencils’ evolution from mobile pioneer to AI-first consultancy.

For more than 20 years, Carmon and Pagon have built, scaled, and sold consulting businesses together—from growing Brulant from a 25-person startup into a 500-person powerhouse, to doubling Rosetta’s scale post-acquisition. As an Operating Partner at Next Sparc, Carmon has spent the last decade turning strategy into results across a portfolio of high-growth ventures. Now, he brings that same transformation expertise to Robots & Pencils.

“Nathan is the rare kind of operator who makes complex things simple and ambitious things achievable,” said Leonard Pagon, CEO of Robots & Pencils and President of Next Sparc. “When we were building Brulant, I could count on Nathan to turn big vision into day-to-day momentum. We scaled fast because he knew how to align teams, deliver value, and move with speed and precision. With Nathan as COO, we’re not just ready for what’s next—we’re built for it.”

Carmon joins at a time when every business is under pressure to adopt AI, modernize legacy systems, and compete at digital speed. Robots & Pencils is meeting that moment with a challenger mindset, deploying high-impact, Navy SEAL-style teams that combine elite engineering with intuitive UX design. With global delivery across North America, Latin America, and Eastern Europe, and partnerships with AWS, Salesforce, and Databricks, the firm is uniquely positioned to help clients lead in the age of AI.

“I’ve helped scale consulting firms from the ground up, and I know what great looks like,” said Carmon. “Robots & Pencils has it. The talent here is exceptional, the leadership is bold, and the market timing couldn’t be better. This team has everything we need to do it again—only bigger, faster, and smarter.”

“With Nathan leading our internal operations, I’m freed up to spend more time where I create the most value—engaging directly with clients, exploring strategic opportunities, and helping shape the future of Robots & Pencils to meet market demand,” said Pagon. “That’s the power of having a leader you trust.”

Carmon holds a BS in Computer Science and an MBA from the University of Michigan. When he’s not accelerating digital transformations, he enjoys wake surfing, biking, model rocketry, and spending time with his daughter and four grandchildren.

July 26, 2025

Robots & Pencils Earns AWS Qualified Software Distinction as an AWS Partner

Robots & Pencils

Recognition spotlights firm’s AWS-native innovation and its mission to help clients modernize fast, scale smarter, and activate AI

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native web, mobile, and app modernization, today announced that it has been recognized as an AWS Partner with an AWS Qualified Software solution. By earning this designation, Robots & Pencils proves its strength in designing AWS-native platforms that are fast, secure, and purpose-built for the AI era.

The AWS Partner Network (APN) is a global community that leverages AWS technologies, programs, and expertise to build solutions that accelerate customer outcomes. With this AWS Partner designation and Qualified Software distinction, Robots & Pencils proves it can meet the highest standards for security, reliability, and operational excellence while outpacing traditional global systems integrators in speed, precision, and innovation.

“We believe the future belongs to companies that can move fast, modernize wisely, and integrate AI seamlessly, and that future runs through AWS,” said Leonard Pagon, CEO of Robots & Pencils. “This recognition is more than a milestone. It’s validation of the demanding work our engineers and designers have put into building intelligent, cloud-native solutions that scale with confidence.”

Robots & Pencils has been delivering solutions on AWS for more than a decade, with a track record of more than 100 successful projects across industries. From data center exits to AI-powered applications, Robots & Pencils supports clients across every phase of digital modernization with AWS. The firm’s software solutions—developed using proven AWS services like AWS Lambda, Amazon API Gateway, Amazon RDS, DynamoDB, and Amazon EventBridge— enables clients to rapidly shift from legacy infrastructure to cloud-native environments that are secure by design, built to evolve—and delivered without the drag of bloated teams or outdated methods.

“We build with purpose. Our teams don’t just plug in services; they architect solutions that solve complex problems and scale in the real world,” said Mark Phillips, Chief Technology Officer at Robots & Pencils. “Being recognized as an AWS Partner with an AWS Qualified Software solution reflects the technical rigor, security focus, and customer impact we bring to every project. This is how we deliver meaningful change for our clients.”

With delivery centers across North America, Latin America, and Eastern Europe, Robots & Pencils partners with organizations in industries including Education, Energy, Financial Services, Healthcare, Retail and Consumer Goods, Technology, and Transportation, to re-architect systems, accelerate time to value, and lay the groundwork for intelligent, scalable growth.

“We’re proud to be part of the AWS Partner Network and to contribute software that helps clients take full advantage of the cloud,” added Pagon. “Whether it’s launching AI-enabled workflows, eliminating technical debt, or modernizing at scale—this is what we were built to do.”

July 26, 2025

Designing for the Unpredictable: An Introduction to Emergent Experience Design

Tyler Klein

Why Generative AI Requires Us to Rethink the Foundations of User-Centered Design

User-centered design has long been our north star—grounded in research, journey mapping, and interfaces built around stable, observable tasks. It has been methodical, human-centered, and incredibly effective—until now.

LLM-based Generative AI and Agentic Experiences, have upended this entire paradigm. These technologies don’t follow predefined scripts. Their interfaces aren’t fixed, their user journeys can’t be mapped, and their purpose unfolds as interaction happens. The experience doesn’t precede the user—it emerges from the LLM’s interaction with the user.

This shift demands a new design framework—one that embraces unpredictability and builds adaptive systems capable of responding to fluid goals. One that doesn’t deliver rigid interfaces, but scaffolds flexible environments for creativity, productivity, and collaboration. At Robots & Pencils, we call this approach Emergent Experience Design.

The Limits of Task-Based UX

Traditional UX design starts with research that discovers jobs to be done. We uncover user goals, design supporting interfaces, and optimize them for clarity and speed. When the job is known and stable, this approach excels.

But LLM-based systems like ChatGPT aren’t built for one job. They serve any purpose that can be expressed in language at run-time. The interface isn’t static. It adapts in real time. And the “job” often isn’t clear until the user acts.

If the experience is emergent, our designs need to be as well.

Emergent Experience Design: A UX Framework for Generative AI

Emergent Experience Design is a conceptual design framework for building systems that stay flexible without losing focus. These systems don’t follow scripts—they respond.

Adapt to user goals in real time

Respond intelligently to unpredictable behavior

Stay aligned to intended outcomes without relying on rigid structures

To do that, they’re built on three types of components:

1. Open Worlds

Open worlds are digital environments intentionally designed to invite exploration, expression, and improvisation. Unlike traditional interfaces that guide users down linear paths, open worlds provide open-ended sandboxes for users to work freely—adapting to user behavior, not constraining it. They empower users to bring their own goals, define their own workflows, and even invent new use cases that a designer could never anticipate.

To define these worlds, we begin by choosing the physical or virtual space—a watch, a phone, a desktop computer, or even smart glasses. Then, we can choose one or more interaction design metaphors for that space—a 3D world, a spreadsheet grid, a voice interface, etc. A design vocabulary then defines what elements can exist within that world—from atomic design elements like buttons, widgets, cells, images, or custom inputs, to more expressive functionality like drag-and-drop layouts, formula editors, or a dialogue system.

Finally, open worlds are governed by a set of rules that control how objects interact. These can be strict (like physics constraints or permission layers) or soft (like design affordances and layout behaviors), but they give the world its internal logic. The more elemental and expressive the vocabulary and rules are, the more varied and creative the user behavior becomes.

Different environments will necessitate different component vocabularies—what elements can be placed, modified, or triggered within the world. By exposing this vocabulary via a structured interface protocol (similar to Model-Context-Protocol, or MCP), LLM agents can purpose-build new interfaces in the world responsively based on the medium. A smartwatch might expose a limited set of compact controls, a desktop app might expose modal overlays, windows or toolbars, and a terminal interface might offer only text-based interactions. Yet from the agent’s perspective, these are just different dialects of the same design language—enabling the same user goal to be rendered differently across modalities.

Open worlds don’t prescribe a journey—they provide a landscape. And when these environments are paired with agents, they evolve into living systems that scaffold emergent experiences rather than dictate static ones.

2. Assistive Agents

Assistive agents are the visible, intelligent entities that inhabit open worlds and respond to user behavior in real time. Powered by large language models or other generative systems, these agents act as collaborators—interpreting context, responding to inputs, and acting inside (and sometimes outside) the digital environment. Rather than relying on hardcoded flows or fixed logic, assistive agents adapt dynamically, crafting interactions based on historical patterns and real-time cues.

Each assistive agent can be shaped by two key ingredients:

Instinct: The training and architecture of the underlying LLM model, which provides its foundational capabilities. This could include the ability to understand text or image inputs, the language in which it responds, and its underlying reasoning patterns.

Identity: The purpose and personality assigned through prompt instructions and contextual inputs that shape the agent’s perspective—what it knows, how it prioritizes information, and how it speaks or acts.

These two ingredients work together to shape agent behavior: instinct governs what the model can do, while identity defines what it should do in a given context. Instinct is durable—coded in the model’s training and architecture—while identity is flexible, applied at runtime through prompts and context. This separation allows us to reuse the same foundation across wildly different roles and experiences, simply by redefining the agent’s identity.

Agents can perceive a wide variety of inputs from typed prompts or voice commands to UI events and changes in application state—even external signals from APIs and sensors. Increasingly, these agents are also gaining access to formalized interfaces—structured protocols that define what actions can be taken in a system, and what components are available for composition. One emerging standard, the Model-Context-Protocol (MCP) pattern introduced by Anthropic, provides a glimpse of this future: an AI agent can query a system to discover its capabilities, understand the input schema for a given tool or interface, and generate the appropriate response. In the context of UI, this approach should also open the door to agents that can dynamically compose interfaces based on user intent and a declarative understanding of the available design language.

Importantly, while designers shape an agent’s perception and capabilities, they don’t script exact outcomes. This allows the agent to remain flexible and resilient, and able to improvise intelligently in response to emergent user behavior. In this way, assistive agents move beyond simple automation and become adaptive collaborators inside the experience.

The designer’s job is not to control every move the agent makes, but to equip it with the right inputs, mental models, and capabilities to succeed.

3. Moderating Agents

Moderating agents are the invisible orchestration layer of an emergent system. While assistive agents respond in real time to user input, moderating agents maintain focus on long-term goals. They ensure that the emergent experience remains aligned with desired outcomes like user satisfaction, data completeness, business objectives, and safety constraints.

These agents function by constantly evaluating the state of the world: the current conversation, the user’s actions, the trajectory of the interaction, and any external signals or thresholds. They compare that state to a defined ideal or target condition, and when gaps appear, they nudge the system toward correction. This could take the form of suggesting a follow-up question to an assistant, prompting clarification, or halting actions that risk ethical violations or user dissatisfaction.

Moderating agents are not rule-based validators. They are adaptive, context-aware entities that operate with soft influence rather than hard enforcement. They may use scoring systems, natural language evaluations, or AI-generated reasoning to assess how well a system is performing against its goals. These agents often manifest through lightweight interventions—such as adjusting the context window of an assistive agent, inserting clarifying background information, reframing a prompt, or suggesting a next step. In some cases, they may even take subtle, direct actions in the environment—but always in ways that feel like a nudge rather than a command. This balance allows moderating agents to shape behavior without disrupting the open-ended, user-driven nature of the experience.

Designers configure moderating agents through clear articulation of intent. This can include writing prompts that define goals, thresholds for action, and strategies for response. These prompts serve as the conscience of the experience—guiding assistants subtly and meaningfully, especially in open-ended contexts where ambiguity is the norm.

Moderating agents are how we bring intentionality into systems that we don’t fully control. They make emergent experiences accountable, responsible, and productive without sacrificing their openness or creativity.

From Intent to Interface: The Role of Protocols

The promise of Emergent Experience Design doesn’t stop at agent behavior—it extends to how the experience itself is constructed. If we treat user goals as structured intent and treat our UI vocabulary as a query-able language, then the interface becomes the result of a real-time negotiation between those two forces.

This is where the concept of Model-Context-Protocol becomes especially relevant. Originally defined as a mechanism for AI agents to discover and interact with external tools, MCP also offers a compelling lens for interface design. Imagine every environment—from mobile phones to smartwatches to voice UIs—offering a structured “design language” via an MCP server. Agents could then query that server to discover what UI components are supported, how they behave, and how they can be composed.

A single requirement—say, “allow user to log in”—could be expressed through entirely different interfaces across devices, yet generated from the same underlying intent. The system adapts not by guessing what to show, but by asking what’s possible, and then composing the interface from the capabilities exposed. This transforms the role of design systems from static libraries to living protocols, and makes real-time, device-aware interface generation not just feasible, but scalable.

A Mindset Shift for Designers

In this new paradigm, interfaces are no longer fixed blueprints. They are assembled at runtime based on emerging needs. Outcomes are not guaranteed—they are negotiated through interaction. And user journeys are not mapped—they are discovered as they unfold. This dynamic, improvisational structure demands a design framework that embraces fluidity without abandoning intention.

As designers, we have to move from architects of static interfaces to cultivators of digital ecosystems. Emergent Experience Design is the framework that lets us shape the tools and environments where humans co-create with intelligent assistants. Instead of predicting behavior, we guide it. Instead of controlling the path, we shape the world.

Why It Matters

Traditional UX assumes we can observe and anticipate user goals, define the right interface, and guide people efficiently from point A to B. That worked—until GenAI changed the rules.

In agentic systems, intent is fluid. Interfaces are built on the fly. Outcomes aren’t hard-coded—they unfold in the moment. That makes our current design models brittle. They break under uncertainty.

Emergent Experience Design gives us a new toolkit. It helps us move from building interfaces for predefined jobs to crafting systems that automate discovery, collaboration, and adaptation in real time.

With this framework, we can:

Meet users where they are—not where we expect them to be

Guide them through complex systems with responsive, context-aware support

Preserve creativity, flexibility, and human agency at every step

In short: it lets us design with the user, not just for them. And in doing so, it unlocks entirely new categories of experience—ones too dynamic to script, and too valuable to ignore.

July 22, 2025

Accomplished Tech Leader Eric Ujvari Joins Robots & Pencils as Lead Solutions Architect

Robots & Pencils

From Fortune 500 transformation to nimble innovation, Ujvari brings digital systems expertise to deepen client trust and accelerate value delivery.

Robots & Pencils, an AI-first, global digital innovation firm specializing in cloud-native mobile, web, and app modernization, today announced that Eric Ujvari has joined the company as Lead Solutions Architect. With over 20 years of experience leading enterprise technology, innovation, and consulting initiatives, Ujvari steps into a key role designed to deepen the company’s ability to bring technical strategy and execution earlier in the client journey.

In this new position, Ujvari will help shape the future of digital transformation by acting as a trusted conduit between business stakeholders and technical teams, ensuring technical decisions align with long-term goals and drive meaningful outcomes from day one.

“Eric’s ability to translate business vision into technical architecture is unmatched,” said Leonard Pagon, CEO of Robots & Pencils. “He doesn’t just understand complex systems—he knows how to simplify and scale them. He asks insightful questions, listens deeply, and has a rare talent for making complex ideas refreshingly easy to understand. He’s the kind of architect every client wants in the room, and every engineer wants on the team. He’s here to help our clients move faster, with more confidence, and I’m thrilled to have him on board.”

Ujvari’s arrival marks the latest step in Robots & Pencils’ evolution from mobile pioneer to AI-first consulting powerhouse. Known for deploying small, high-impact teams with elite engineering talent, the firm is rapidly expanding its ability to blend intuitive UX with future-ready, AI-infused digital platforms. Ujvari will play a key role in helping clients recognize opportunities earlier and design systems that scale.

“I’m excited to be joining a dynamic organization whose mission is to push the technological and operational boundaries for current and future client partners,” said Ujvari. “Having the opportunity to collaborate with such a talented, nimble team of engineers, designers, AI specialists, and digital product professionals is something I’m truly looking forward to. I see this role as a chance to help showcase the best of Robots & Pencils to the world—through thoughtful architecture, collaboration, and innovation.”

Before joining Robots & Pencils, Ujvari played a pivotal role in scaling and shaping the Solutions Architecture discipline at WillowTree, contributing at the intersection of commercial strategy, engineering, and delivery. His experience includes leadership roles at Cardinal Health, where he drove large-scale enterprise data strategy and system design initiatives across global supply chain, healthcare, and digital transformation programs. Across roles, he has built a reputation for strategic clarity, collaborative leadership, and an unwavering commitment to client value.

As Robots & Pencils accelerates its growth, Ujvari’s addition marks a key inflection point: embedding digital strategy and technical leadership earlier in every client engagement—ensuring better solutions and stronger partnerships.

July 18, 2025

From AI to Execution: Why AWS’s Ecosystem Strategy Matters for You

Christina Morello

Why AWS’s partner play is more powerful than ever—and how Robots & Pencils is positioned to deliver

In our AWS Summit NYC recap series, we’ve explored the rise of agentic AI and the infrastructure upgrades making AI-native systems possible.

But beneath the product announcements and keynote buzz, AWS made something else clear: the strength of your partnerships will define how fast—and how well—you can put this innovation to work.

For Robots & Pencils, the AWS ecosystem isn’t just support—it’s a multiplier for speed, alignment, and delivery.

From Tech Stack to Trust Network

AWS isn’t just launching tools—it’s creating the environment for those tools to thrive. That means:

New partner categories make AI solutions easier to find and deploy

Training tracks and certifications speed delivery

Direct connections between AWS teams, partners, and customers keep strategy aligned

We saw it up close. From booths to breakouts to one-on-one meetings, the AWS Summit felt like an inflection point—not just for the cloud, but for the partner community driving its adoption.

AWS Is Creating the Right Categories for the Right Moment

With the debut of the “AI Agents & Tools” category in AWS Marketplace, partners now have a faster path to visibility—and customers have a clearer path to adoption. This is a win for agile teams with real capabilities, not just market hype, and it reflects something we wrote in the first article in our AWS Summit NYC recap series:

“AWS is moving from models to agents—and that shift demands partners who can build systems that act, not just answer.”

Robots & Pencils is already there, and this new Marketplace category gives us—and our clients—room to move faster.

Training Isn’t Just Available—It’s Evolving

At the summit, we participated in several partner enablement sessions focused on agentic AI, security, and cost-optimized architecture. The message: certification isn’t a checkbox. It’s an edge.

With AWS investing $100M more into its Generative AI Innovation Center, that advantage is about to compound. AWS wants to scale partner-led innovation—and we’re leaning in hard, upskilling across engineering, architecture, and delivery.

In second article in our AWS Summit NYC recap series, we covered the infrastructure that supports AI. Here, we’re talking about the people. AWS knows partner talent is the force multiplier, and so do we.

Relationships Drive Better Results—For Us and Our Client

Our team, including EVP Scott Young, CRO Patrick Higgins, and CTO Mark Phillips, had face time with AWS sales leaders tied to our current clients, plus strategic product and solutions teams. These weren’t “check-in” conversations. They’re forward-looking, roadmap-level discussions built on shared outcomes.

Whether we’re supporting a retail loyalty rebuild or a health tech AI rollout, these connections ensure we can act fast, align fast, and deliver fast. When your partner relationships are strong, the technology moves quicker—and the value lands sooner.

The Ecosystem is the Advantage

We’ve already written about the power of AWS’s newest AI tools and smarter infrastructure. But tools alone don’t create transformation—ecosystems do. That’s what AWS is building, and it’s what we’re investing in.

Robots & Pencils isn’t just an implementer. We’re a strategic partner moving with speed, clarity, and intent—ready to deliver value inside the AWS ecosystem.

Want to move fast and scale smart? Let’s connect.

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