1. Design the system around bounded tasks, not a single super-agent
Production systems are safer when each agent owns a narrow job: classify intent, fetch context, generate options, or draft output. A controller then decides which agent to call and when to stop. This keeps prompt scope smaller, cuts token waste, and makes failures easier to isolate.
2. Build hard guardrails at runtime
Put controls in code, not only in prompts. Enforce allowed tools, schema validation, timeout limits, max iteration count, and fallback responses. Treat tool calls like API calls from untrusted code: authorize, validate input, and log every step.
- Use JSON schema validation for every agent output before downstream execution.
- Block high-risk actions unless a human approves.
- Use deterministic fallback paths for low-confidence states.
3. Add agent observability from day one
Traditional app telemetry is not enough. You need traces per conversation, tool-level latency, token usage, retry counts, and failure reason taxonomy. Without this, the team cannot improve quality with confidence.
We recommend three operational dashboards: reliability (error rates, retries, hallucination flags), speed (time to first useful response), and cost (token and tool spend per successful task).
4. Evaluate with scenario suites, not one benchmark score
Model quality changes by domain, user intent, and tool chain complexity. Build a scenario suite from real production cases and score outcomes against explicit pass criteria.
- Golden set for frequent requests.
- Edge set for risky or ambiguous instructions.
- Regression set to protect critical workflows before each release.
5. Align KPIs to business outcomes
Agent projects fail when teams track only technical stats. Track business-level impact: deflection rate, cycle-time reduction, conversion lift, and user-reported trust. Instrument these metrics before launch so baseline and post-launch performance are comparable.