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88% of Agent Pilots Never Reach Production: Run This Exit Checklist Before Yours Does

#ai #testing #devops #agents

A Teradata survey of 1,000 global technology leaders found that only 7% of enterprises reached AI operational maturity and 88% of agent pilots never entered production. The top three barriers were evaluation capability (64%), governance friction (57%), and model reliability (51%). This article turns those findings into a pre-production exit checklist you can run against your own agent pilot.

What the data says

Barrier Reported percentage What it means for your pilot
Evaluation capability 64% You cannot measure whether the agent improved outcomes
Governance friction 57% Security, compliance, or audit review blocked the launch
Model reliability 51% The agent's output was too inconsistent for production

If your pilot has not addressed all three, it will join the 88%.

The exit checklist

Run each check and record the evidence. A pilot passes only when every gate has a concrete answer.

Gate 1: Evaluation capability 

[ ] Defined a baseline metric before the pilot started
[ ] Collected matched pairs (with-agent vs without-agent) for the same task
[ ] Measured false-positive and false-negative rates, not just success rate
[ ] Documented the evaluation harness, dataset, and scoring rubric
[ ] A second reviewer can reproduce the evaluation independently
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If any box is unchecked, the pilot cannot prove its value. "It felt faster" is not evidence.

Gate 2: Governance 

[ ] Identified the data classification of every input the agent reads
[ ] Confirmed no production secrets are accessible to the agent's execution role
[ ] Documented the rollback procedure for agent-initiated changes
[ ] Defined an audit log schema that records agent actions with timestamps
[ ] Security review completed and findings addressed or accepted
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Gate 3: Model reliability 

[ ] Ran the agent on 100+ real tasks and recorded the failure rate
[ ] Identified the top 3 failure modes and their root causes
[ ] Defined a maximum acceptable failure rate for production
[ ] Implemented a fallback path for when the agent fails or is uncertain
[ ] Tested the fallback path under load
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Evidence record template 

{
  "pilot_name": "code-review-agent-pilot",
  "evaluated_at": "2026-07-20T10:00:00Z",
  "baseline_metric": {
    "metric": "mean_time_to_first_review_comment",
    "without_agent_seconds": 1800,
    "with_agent_seconds": 1200,
    "sample_size": 50
  },
  "failure_modes": [
    {"mode": "hallucinated_api", "count": 3, "root_cause": "outdated_docs"},
    {"mode": "missed_security_issue", "count": 1, "root_cause": "no_sec_training_data"}
  ],
  "governance": {
    "data_classification": "internal",
    "secrets_exposed": false,
    "rollback_tested": true,
    "audit_log_schema": "v1"
  },
  "max_failure_rate": 0.05,
  "observed_failure_rate": 0.08,
  "gate_status": "FAIL",
  "failure_reason": "observed_failure_rate exceeds max_failure_rate"
}
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Common pilot exit errors

  • Survivorship bias: measuring only tasks where the agent succeeded and discarding failures
  • Moving baseline: changing the comparison metric after seeing agent results
  • No fallback: the pilot assumes the agent always works, so there is no degradation path
  • Evaluation by the builder: the same person who built the agent also scored it

What to check

Can you fill in the evidence record above with real numbers from your current pilot? If you cannot fill in observed_failure_rate, you are in the 64% who cannot evaluate--and likely in the 88% who never reach production.

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