1. Align on the human handshake
Every agent begins with a crafted handshake between human and system. Define the decision surface, escalation paths, and audit points before writing prompts. High-confidence moments should feel effortless; risky branches must surface context for human arbitration.
- Map personas, objectives, and guardrails in a shared brief
- Document the ground-truth corpus your agent can rely on
- Prototype the UX around failure states, not just happy paths
2. Build in instrumentation from day zero
Agentic systems demand measurable behavior. Instrument state transitions, tool calls, and user feedback before launching pilots. This dataset powers your evaluation suite, regression tests, and future training cycles.
- Log structured traces for every tool invocation
- Capture human edits to create reinforcement datasets
- Publish real-time dashboards for latency, success, and overrides
3. Ship to a trusted cohort fast
Find the earliest group willing to co-create. Deliver value within one or two cycles, then iterate with daily touchpoints. Cohort notes reveal policy gaps, missing context, and latency ceilings you cannot predict in isolation.
4. Treat evaluation as a product
Use synthetic tests for guardrails, replay traces for regression, and human review for nuanced decisions. Bundle these into a CI gate so each prompt or tool change ships with evidence.
- Scenario suites: synthetic + recorded conversations
- Metrics: adherence, hallucination rate, escalation latency
- Human review: lightweight rubric in Notion or Productboard
5. Launch with a runway for iteration
Successful agent launches pair compelling messaging with an explicit learning plan. Communicate what the agent does today, what it is learning next, and how you handle edge cases. Maintain a public changelog to reinforce trust.
How I engage
I embed with product teams to craft this full lifecycle—strategy, architecture, implementation, and iteration. If you need a partner to operationalize agentic software, reach out.