Enterprise AI projects rarely fail because of model quality alone. They fail when orchestration, observability, and governance are treated as afterthoughts.
Define narrow responsibilities
Agentic systems become unstable when one agent is asked to do everything. We split workflows into distinct roles:
- Planner: decides sequence and constraints
- Executor: performs scoped tasks
- Verifier: checks outputs against policy and expected format
This keeps behavior predictable and makes each stage testable.
Build with deterministic guardrails
Production systems need deterministic boundaries around non-deterministic models. We enforce:
- strict JSON output contracts
- retries with capped backoff
- confidence thresholds before downstream actions
- safe fallbacks when validation fails
The goal is graceful degradation, not perfect output.
Observe everything that matters
A useful trace is more than latency metrics. We track:
- prompt and context versions
- tool calls and side effects
- validation failures by category
- escalation paths to human review
This creates a feedback loop for tuning prompts, policies, and routing logic.
Final takeaway
Agentic workflows scale when they are treated as systems engineering problems. Architecture, controls, and measurement should lead model choice, not follow it.