AI Agent Retraining Signals | When to Retrain

Executive Summary

Direct answer: Retrain when performance degrades beyond agreed tolerances and fixes to prompts, policies, or data quality don’t restore results. Signals include sustained KPI decline versus control, rising escalation or policy-failure rates, concept/data drift (new products, pricing, taxonomy), repeated corrective feedback, or changes to tools/APIs the agent relies on. Confirm with an evaluation suite and only then schedule dataset, embedding, or model retraining.

Guiding Principles

1
Track KPI deltas vs. control across cohorts
2
Watch policy failures and escalation trends
3
Detect data/schema drift in source systems
4
Audit feedback loops for repeated corrections
5
Re-test with evals before scheduling retrain
Prefer prompt, policy, data, or embedding updates before model retraining; change the smallest thing that fixes the problem.

Retraining Readiness Checklist

  • KPI drop persists 2+ cycles vs. control
  • Policy/brand/accessibility failures are rising
  • Data model or taxonomy changed recently
  • New offers, pricing, or products launched
  • Tool/API behavior or latency shifted
  • User feedback shows recurring mistakes
  • Embeddings older than freshness window
  • Prompt/policy/data fixes failed to recover

What to Fix First (Expanded)

Not every dip requires retraining. Start with the least-disruptive fixes: validate data freshness and field mapping, tighten prompts and guardrails, and re-index knowledge (embeddings) before considering model changes. Use an evaluation suite that mirrors real work—task success, tone/brand checks, policy compliance, and cost/latency—to compare the current agent against a control.


If KPIs remain below tolerance and errors cluster around knowledge updates or new patterns (e.g., product launch, pricing change, regional rules), schedule targeted retraining: refresh training data, rebuild embeddings, or fine-tune components implicated by the failures. Operationally, define ownership, thresholds, and cadence (e.g., monthly embedding refresh; quarterly model review; ad-hoc retrain on major releases). Record provenance, version numbers, and reason codes so you can roll back safely if results regress.


Why TPG? We build governed eval suites, data pipelines, and promotion gates across enterprise MAP/CRM stacks so teams improve accuracy without unnecessary retrains.

Metrics & Benchmarks

Metric Formula Target/Range Stage Notes
KPI delta vs. control Variant KPI ÷ control Within tolerance or ↑ Monitor Tracked per cohort
Policy fail rate Failed checks ÷ total checks Trending down Govern Brand/privacy/accessibility
Escalation rate Escalations ÷ sensitive actions Stable or ↓ Operate Signals trust/reliability
Drift score PSI/KL on key fields Within bounds Detect Indicates data shift
Eval pass rate Passed tests ÷ total evals ≥ baseline Validate Run pre/post changes

Additional Resources

Agentic AI Overview How AI Agents Remember Context Contact The Pedowitz Group

Frequently Asked Questions

What should I try before retraining?

Fix data freshness, update prompts/policies, and re-embed knowledge; re-run evals to confirm recovery.

How often should embeddings be refreshed?

Refresh on content changes and on a cadence (e.g., monthly) for dynamic domains.

When is fine-tuning justified?

When persistent style or task errors remain after prompt and data improvements and evals confirm the gap.

How do I avoid thrash from frequent retraining?

Gate on stable signals across two or more measurement cycles and require eval pass before promoting a retrain.

Do new tools require retraining?

Sometimes. Start with tool-use prompts and simulations; retrain only if errors persist after policy and prompt updates.

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