AI CI/CD with Harness: End-to-End Blueprint

AI delivery needs software discipline plus model-aware controls. This blueprint outlines a practical CI/CD architecture for teams running LLM-powered products with Harness.

Architecture overview

Developer PR
  -> CI build + unit tests
  -> Prompt checks + config validation
  -> Offline eval suite
  -> Security/policy validation
  -> Staging deployment
  -> Online canary verification
  -> Progressive prod rollout

Artifact model

App artifact: API/service code.
Prompt artifact: templates, system prompts, tool instructions.
Evaluation artifact: dataset version + scoring outputs.
Config artifact: model routing, fallback, budget limits.

Release decision matrix

Condition	Decision
Quality improves, cost stable	Promote
Quality stable, cost rises sharply	Manual approval required
Critical scenario regression	Reject
High latency during canary	Pause rollout

Suggested Harness stage layout

Stage A: Build
Stage B: Static checks (prompt, schema, policy)
Stage C: Eval gate (critical + full suites)
Stage D: Deploy Staging
Stage E: Verify (latency, error rate, quality probe)
Stage F: Canary + auto-rollback
Stage G: Full rollout + post-deploy report

Observability signals to keep

Prompt version in every request trace.
Model route and fallback reason in logs.
Token usage and cost by endpoint.
User feedback score by intent category.

Rollback strategy

Rollback prompt/config first when app code is unchanged.
Fallback to previous stable model route.
Throttle high-risk endpoints while incident is open.
Re-run critical eval suite before unpausing rollout.

Takeaway

AI CI/CD succeeds when release decisions are metric-driven and reversible. Harness gives the stage orchestration; your team must provide strong eval signals and clear rollback rules.