System boundaries
- where agents should and should not operate
- which tools are exposed
- where deterministic code takes over
implementation / build track
AI implementation consultant for teams shipping production agents.
Hands-on AI implementation for routing, evals, fallbacks, and release discipline.
As an AI implementation consultant, I help teams turn agent prototypes into production systems with clear routing, eval loops, fallback paths, and release discipline.
- → Tool routing
- → Eval loops
- → Fallbacks
- → Release gates
What gets built
The work is operational. Fewer moving parts. Better defaults. Clear failure paths.
Eval discipline
- test cases tied to real failures
- score thresholds that block bad releases
- repeatable comparisons across prompts and models
Reliability layer
- timeouts, retries, and fallback paths
- observability that explains behavior
- incident paths that are not guesswork
Operating defaults
- less configuration
- fewer switches with unclear owners
- faster iteration without hidden breakage
Typical deliverables
System map
Routing decisions, ownership boundaries, and failure points that fit the actual product.
Release controls
Evals, thresholds, and rollout rules for new prompts, models, and tools.
Operational runbooks
Escalation paths, rollback rules, and fallback expectations for when the system fails.
Platform cleanup
Simpler primitives so teams can ship without adding another layer of prompt plumbing.