Agentic AI: Mistakes to avoid — the traps behind 70% of project failures

According to McKinsey (2024), AI projects fail mainly for organizational—not technical—reasons. Agentic AI amplifies risk because it acts autonomously, uses multiple tools, and chains many decisions. Understanding the common errors helps prevent production incidents.

• Vague mission, unclear objectives, no metrics.
• MIT Sloan (2023): agents without explicit goals drift or become unusable.

Human steering is essential. Without an owner:

• the agent improvises, accountability is unclear, errors accumulate.
• NIST AI RMF (2023): continuous human oversight is mandatory for autonomous systems.

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An agent only follows what is described, documented and structured. Implicit rules (“we’ve always done it this way”) lead to wrong behavior.

• OECD AI Principles (2023): autonomous systems require explicit business logic.

Agentic AI depends on data. If data is outdated, incomplete, scattered or undocumented, the agent makes poor decisions.

• Deloitte (2024): data silos are the #1 obstacle to deploying Agentic AI.

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Agentic AI can execute real-world actions. Without guardrails: cascading bad decisions, uncontrolled access, no traceability.

• NIST AI RMF (2023) recommends action limits, human escalation and full logging.

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Agents must be tested in ambiguous, imperfect, unexpected and contradictory scenarios.

• OpenAI (2024): agents should be tested in sandboxes before real use.

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In reality it transforms them: governance, steering, supervision and exception management.

• World Economic Forum (2024): it creates hybrid roles rather than removing responsibility.

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Agents must be re-evaluated, recalibrated, updated and monitored.

• NIST (2023): autonomous systems drift without regular reassessment (model drift and process drift).