Guardrails That Actually Work: Evaluations + Human-in-the-Loop

“Guardrails” aren’t magic filters—they’re process design: small-scope steps, evaluation gates, and clear rollback.

Why this matters now

As agents touch more of your stack, the risk isn’t a single bad response—it’s silent drift across hundreds of small actions. Guardrails that work in production are boring by design: each step does one thing; evaluations catch regressions early; humans approve the right stuff (not everything). When problems happen, you can roll back quickly with a clean audit trail.

How to design reliable agents

Small-scope steps: classify, extract, draft, validate. Use deterministic actions (APIs) wherever possible; reserve AI for fuzzy judgment.
Evaluation harness: unit evals (does extraction hit F1 ≥ threshold?) + scenario evals (end-to-end tasks with business rubrics). Keep golden sets small but curated; refresh monthly.
Human-in-the-loop: define approval levels—auto, fast-review, expert-review. Show diffs instead of whole docs and require a one-line rationale on overrides; write that back to the KG.
Change control: version prompts/policies; ship behind feature flags; enable one-click rollback and notifications.

Example / How-to (drop-in design)

• Levels:
  • auto: score ≥0.85, low risk.
  • fast_review: 0.70–0.85 or medium risk.
  • expert_review: <0.70 or high risk term detected.
• Metrics to track: corrections per artifact, time-to-accept, rollback rate, and “unreviewed-but-published” (should be ~0).
• Playbook: on drop in eval score or spike in rollbacks → freeze new publishes, switch to conservative prompt, alert owners, open incident notes.

Next steps

• Map one workflow to four steps; add evals for two steps this week.
• Introduce HITL levels and ship a diff-based review UI.
• Add rollback and alerting; run a tabletop drill.
• Want a governance checklist + example golden set? Download the pack or schedule a review.