Multi-stage agent workflows fail differently than single-shot chat. When every stage depends on the last, end-to-end debugging becomes a whodunit — and models will narrate confident conclusions on top of broken middles.

These posts cover how we bring up, orchestrate, and verify production agent pipelines.

Part of the series Building an Enterprise AI Agent Platform in Go.

Post What you’ll learn
Bring Up Agent Workflows Like Hardware Green one stage at a time; golden gates; score effects not transcripts
Prove, Then Narrate — Evidence-Gated Multi-Plane RCA Fixed DAG, structural evals, compound-AI orchestration for SRE RCA
Evidence-Based Verification Don’t trust self-report — check ArgoCD, Datadog, systems of record

FAQ

How do you debug a multi-stage AI agent workflow?

Bring up one stage at a time against a golden gate — like hardware board bring-up. Green each stage repeatedly before adding the next. Score committed tool calls, not raw transcripts.

What is evidence-gated agent orchestration?

Wrap frontier models in a fixed DAG with structural evals, state merging, and token-aware tool loops. Let Go own pass/fail; let the model narrate only after evidence is committed.

How do you verify agent workflow outcomes in production?

Pull evidence from systems of record — ArgoCD, Datadog, Grafana — instead of trusting self-reported success. Verification gates should be deterministic where possible.