Agentic Delivery — Dossier

Snapshot: an execution system that reliably converts PRDs into weekly, measurable releases by combining disciplined specs, automated guardrails, and a simple but rigorous QA gate.

Where agentic delivery helps / doesn't

  • Helps: predictable release cadence, rapid validation of hypotheses, low-friction incremental shipping, and automated safety checks to reduce manual rollback work.
  • Doesn't: open-ended research, complex customer negotiations, or tasks requiring deep, one-off expert judgement where exploration is the primary goal.
  • Good fit: product improvements, small end-to-end features, experiment rollouts, and operational automation.

Delivery loop (practical)

  1. Spec & Acceptance Criteria: one-paragraph intent + 3–5 acceptance criteria (functional, perf, safety, observability).
  2. Break into components: identify minimal surface area, API/contract, and data requirements.
  3. Implement + component tests: write focused unit/integration tests; keep changes isolated behind feature flags if needed.
  4. Automated guardrails & staging checks: input validation, rate-limits, anomaly detectors, canary rollouts.
  5. QA gate: manual smoke checklist + automated regression suite on staging; signoff by owner.
  6. Release + instrument: promote with feature flag, capture business and technical metrics.
  7. Measure → iterate: compare metrics vs acceptance criteria; decide rollback, tweak, or expand scope.

Acceptance criteria — concrete examples

  • Functional: 100% of N automated scenarios pass (example: 12/12 end-to-end tests).
  • Performance: P95 latency under 200ms at 95th percentile of expected load.
  • Quality: zero new critical/regression failures in staging; non-blocking bugs < 2 for launch.
  • Safety/Privacy: no PII leakage in logs; access controls enforced for new endpoints.
  • Observability: business metric collection enabled (conversion, error rate) and alert thresholds defined.

QA gate — checklist

A short, repeatable gate applied before any release:

  • Build status: green (CI passed)
  • Automated integration tests: all critical flows pass
  • Manual smoke tests: owner runs 5 core scenarios (happy path + 2 edge cases)
  • Observability: metrics & logs emitting; dashboard updated
  • Rollback plan: documented and tested (feature flag or revert playbook)
  • Signoff: product owner or on-call engineer confirms acceptability

Why it works

The system reduces cognitive load by making expectations explicit (specs + acceptance criteria), limits blast radius with guardrails, and makes decisions evidence-driven through instrumentation. Tight loops surface bad assumptions quickly and keep scope manageable.

Concrete example (template)

Feature: improve lesson recommendation relevancy

Spec: Improve lesson recommendations to increase completion rate for new users by surfacing 3 better-fit lessons. Acceptance criteria: - 12/12 automated scenarios pass (recommendation API responds with 3 items matching user intent) - P95 latency < 150ms - Conversion (completion rate) lifts by +5 percentage points in the experiment cohort QA checklist: integration tests, manual UX sanity, metrics dashboard added, rollback plan documented.
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