Gartner’s latest forecast is striking: more than 40% of agentic AI projects will be canceled by 2027. At first glance, this looks like a technology growing faster than it can mature. But a closer look across the industry shows a different pattern. Many initiatives stall for the same reason micromanaged teams do. The work is described at the level of steps rather than outcomes. When expectations aren’t clear, people wait for instructions. When expectations aren’t clear for agents, they either improvise poorly or fail to act.
This is the same shift I described in my previous article, “Software’s Biggest Breakthrough Was Making It Cheap Enough to Waste.” When software becomes inexpensive enough to test freely, the organizations that pull ahead are the ones that work toward clear outcomes and validate their decisions quickly.
Agentic AI is the next stage of that evolution. Autonomy becomes meaningful only when the organization already understands the outcome it’s trying to achieve, how good decisions support that outcome, and when judgment should shift back to a human.
The Shift to Outcome-Oriented Programming
Agentic AI brings a model that feels intuitive but represents a quiet transformation. Traditional automation has always been procedural in that teams document the steps, configure the workflow, and optimize the sequence. Like a highly scripted form of people management, this model is effective when the work is predictable, but limited when decisions are open-ended or require problem solving.
Agentic systems operate more like empowered teams. They begin with a desired outcome and use planning, reasoning, and available tools to move toward it. As system designers, our role shifts from specifying every step to defining the outcome, the boundaries, and the signals that guide good judgment.
Instead of detailing each action, teams clarify:
- What the outcome should be
- How success will be measured
- Which contextual signals matter
- Where the boundaries and escalation points are
This shift places new demands on organizational clarity. To support outcome-oriented systems, teams need a shared understanding of how decisions are made. They need to determine what good judgment looks like, what tradeoffs are acceptable, and how to recognize situations that require human involvement.
Industry research points to the same conclusion. Harvard Business Review notes that teams struggle when they choose agentic use cases without first defining how those decisions should be evaluated. XMPRO shows that many failures stem from treating agentic systems as extensions of existing automation rather than as tools that require a different architectural foundation. RAND’s analysis adds that projects built on assumptions instead of validated decision patterns rarely make it into stable production.
Together, these findings underscore a simple theme. Agents thrive when the organization already understands how good decisions are made.
Decision Intelligence Shapes Agentic Performance
Agentic systems perform well when the outcome is clear, the signals are reliable, and proper judgment is well understood. When goals or success criteria are fuzzy, or tasks overly complex, performance mirrors that ambiguity.
In a Carnegie Mellon evaluation, advanced models completed merely one-third of multi-step tasks without intervention. Meanwhile, First Page Sage’s 2025 survey showed much higher completion rates in more structured domains, with performance dropping as tasks became more ambiguous or context heavy.
This reflects another truth about autonomy. Some problems are simply too broad or too abstract for an agent to manage directly. In such cases, the outcome must be broken into sub-outcomes, and those into smaller decisions, until the individual pieces fall within the system’s ability to reason effectively.
In many ways, this mirrors effective leadership. Good leaders don’t hand individual team members a giant, unstructured mandate. They cascade outcomes into stratified responsibilities that people can act on. Agentic systems operate the same way. They thrive when the goal has been decomposed into solvable parts with well-defined judgment and guardrails.
This is why organizational clarity becomes a core predictor of success.
How Teams Fall Into the Agentic Trap
Many organizations feel the pull of agentic AI because it promises systems that plan, act, and adapt without waiting for human intervention. But the projects that stall often fall into a predictable trap.
Teams begin by automating process instead of automating the judgment behind the decisions the agent is expected to make. Teams define what a system should do instead of defining how to evaluate the output or what “good” should look like. Vague quality metrics, progress signals, and escalation criteria lead to technically valid, strategically mediocre decisions that erode confidence in the system.
The research behind this pattern is remarkably consistent. HBR notes that teams often choose agentic use cases before they understand the criteria needed to evaluate them. XMPRO describes the architectural breakdowns that occur when agentic systems are treated like upgrades to procedural automation. RAND’s analysis shows that assumption-driven decision-making is one of the strongest predictors of AI project failure, while projects built on clear evaluation criteria and validated decision patterns are far more likely to reach stable production.
This is the agentic trap: trying to automate judgment without first understanding how good judgment is made. Agentic AI is more than automation of steps, it’s the automation of evaluation, prioritization, and tradeoff decisions. Without clear outcomes, criteria, signals, and boundaries to inform decision-making, the system has nothing stable to scale, and its behavior reflects that uncertainty.
A Practical Way Forward: The Automation Readiness Assessment
Decisions that succeed under autonomy share five characteristics. When one or more are missing, agents need more support:
- Decision Understanding: Teams document how good decisions are made: not just the steps, but the criteria, signals, and judgment patterns. If a new teammate could reproduce the decision with consistency, the foundation is strong.
- Validated Patterns: The decision has been tested repeatedly with consistent, measurable results. Variance is understood. Edge cases surface early.
- Success Metrics: Clear thresholds define what “good” looks like, what counts as acceptable variance, and when escalation should occur.
- Data Signals: All required information is available, trustworthy, and accessible from a unified interface. Decisions are only as good as the signals behind them.
- Governance Boundaries: Teams define what the agent may and may not do, when it must escalate, and where human oversight remains essential.
Have all five? Build with confidence.
Only three or four? Pilot with human review in order to build up a live data set.
Only one or two? Go strengthen your decision clarity before automating.
This approach keeps teams grounded. It turns autonomy from an aspirational leap into a disciplined extension of what already works.
The Path to Agentic Maturity
Agentic AI expands an organization’s capacity for coordinated action, but only when the decisions behind the work are already well understood. The projects that avoid the 40% failure curve do so because they encode judgement into agents, not just process. They clarify the outcome, validate the decision pattern, define the boundaries, and then let the system scale what works.
Clarity of judgment produces resilience, resilience enables autonomy, and autonomy creates leverage. The path to agentic maturity begins with well-defined decisions. Everything else grows from there.
The pace of AI change can feel relentless with tools, processes, and practices evolving almost weekly. We help organizations navigate this landscape with clarity, balancing experimentation with governance, and turning AI’s potential into practical, measurable outcomes. If you’re looking to explore how AI can work inside your organization—not just in theory, but in practice—we’d love to be a partner in that journey. Request an AI briefing.
Key Takeaways
- Agentic AI only creates leverage when decisions are already well understood. The strongest projects start from clearly defined outcomes, success metrics, and decision criteria, then give agents room to act within those boundaries.
- Outcome-oriented programming replaces step-by-step scripting. Traditional automation focuses on sequences of tasks. Agentic systems focus on the result, the signals that guide judgment, and the escalation paths that keep risk controlled.
- Organizational clarity is the real performance bottleneck. Agentic systems mirror the quality of the environment around them. Clear outcomes, validated decision patterns, and reliable data signals translate directly into more effective autonomy.
- Many failed projects share one root cause: unarticulated decisions. Initiatives lose momentum when teams automate decisions that have never been documented, measured, or tested, so value becomes hard to demonstrate and risk becomes hard to govern.
- The Automation Readiness Assessment turns autonomy into a staged progression. By evaluating five factors, teams can decide whether to build, pilot with human review, or first strengthen decision clarity before pushing for autonomy.
- Agentic maturity follows a sequence. Clarify outcomes, validate patterns, define governance boundaries, and then scale what works. Clarity of judgment produces resilience, resilience enables autonomy, and autonomy amplifies impact.
FAQs
What is the “agentic trap”?
The agentic trap describes what happens when organizations rush to deploy agents that plan and act, before they have defined the outcomes, decision criteria, and guardrails those agents require. The technology looks powerful, yet projects stall because the underlying decisions were never made explicit.
How is agentic AI different from traditional automation?
Traditional automation follows a procedural model. Teams document a sequence of steps and the system executes those steps in predictable conditions. Agentic AI starts from an outcome, uses planning and reasoning to choose actions, and navigates toward that outcome using tools, data, and judgment signals. The organization moves from “here are the steps” to “here is the result, the boundaries, and the signals that matter.”
Why do so many agentic AI projects lose momentum?
Momentum fades when teams try to automate decisions that have not been documented, validated, or measured. Costs rise, risk concerns surface, and it becomes harder to show progress against business outcomes. Research from Gartner, Harvard Business Review, XMPRO, and RAND all point to the same pattern: projects thrive when the decision environment is explicit and validated, and they struggle when it is based on assumptions.
What makes a decision “ready” for autonomy?
Decisions are ready for agentic automation when they meet five criteria:
- Decision Understanding: Teams can describe how good decisions are made, including criteria and judgment patterns.
- Validated Pattern: The decision has been tested repeatedly with consistent results and known variance.
- Success Metrics: Clear thresholds define acceptable outcomes and escalation conditions.
- Data Signals: Required information is reliable, available, and accessible from a unified interface.
- Governance Boundaries: The system has clear permissioning, escalation rules, and human oversight points.
The more of these elements are present, the more confidently teams can extend autonomy.
How can we use the Automation Readiness Assessment in practice?
Use the five criteria as a simple scoring lens for each candidate decision:
- All five present: advance to build and scale.
- Three or four present: run a pilot with human review to gather live data and refine the pattern.
- One or two present: invest in clarifying and testing the decision before automation.
This keeps investment aligned with decision maturity and creates a clear path from experimentation to durable production.
Where should leaders focus first to reach agentic maturity?
Leaders gain the most leverage by focusing on judgment clarity within critical workflows. That means aligning on desired outcomes, success metrics, escalation thresholds, and the signals that inform good decisions. With that foundation, agentic AI becomes a force multiplier for well-understood work rather than a risky experiment in ambiguous territory.
