Handbook
Agentic SDLC (cross-cutting)
**Agentic SDLC** describes delivery where **software agents** (LLM-based assistants, codegen bots, review bots, test generators) are part of the ** toolchain**, alongside humans. It is **not** a repla
Agentic SDLC (cross-cutting)
What we mean by “agentic”
Agentic SDLC describes delivery where software agents (LLM-based assistants, codegen bots, review bots, test generators) are part of the ** toolchain, alongside humans. It is not a replacement for Scrum, Kanban, XP, or phased models — it is a layer on how work is executed and governed**.
Principles:
- Humans own intent — product goals, acceptance, ethics, and release decisions.
- Agents amplify execution — code, tests, drafts, refactors — under policies (review, CI, security).
- Same lifecycle — Phases A–F (Software development lifecycle (SDLC)) still apply; agents change throughput, not the need for specs, tests, and traceability when your domain requires them.
- Transparency — commits, PRs, and CI should remain inspectable; avoid “black box” changes without audit trail.
Process diagram (handbook)
Human intent → agents/tools → review & CI → repository with work-unit linkage. Chat is not the system of record.
Relationship to engineering tracking
Repos may adopt an optional tracking foundation under sdlc/ (see templates/sdlc/):
| Concept | Role in agentic workflows |
|---|---|
| Contributor | Still human identity (e.g. git email); agents may use bot accounts — policy should say how those are labeled. |
| Events | Commits and CI runs remain evidence; agent-generated commits should be reviewable like any other change. |
| Work units | Link to backlog / REQ ids so automation doesn’t orphan changes from intent. |
Time: commit timestamps are not duration; explicit time logging (if needed) is separate — see TRACKING-FOUNDATION.md (duration section) in projects that include it.
Ceremonies under higher automation
| Ceremony type | What changes | What does not |
|---|---|---|
| Planning | Estimates may need recalibration (smaller slices, faster coding). | Goals and scope still need human agreement. |
| Review / retro | Discussion may include CI flakiness, agent mistakes, prompt hygiene. | Psychological safety and process improvement remain human. |
| Quality | More reliance on automated gates; human review for riskier areas. | Definition of Done still enforced. |
Risks (see also project TRACKING-CHALLENGES)
- Review bottleneck — agent speed outpaces human review; address with smaller PRs, pairing, WIP limits on review.
- Metric gaming — commit volume can rise without value; prefer work-unit completion and outcomes.
- Compliance — regulated domains still need sign-offs and evidence outside chat logs.
External reading (tools and practice)
| Topic | Starting point | Executive summary (why it’s linked here) |
|---|---|---|
| AI + SDLC (general) | Search for “LLM software development lifecycle” + your stack; prefer vendor-neutral guidance for policy. | No single canonical URL—find guidance that matches your stack and policy; avoid vendor lock-in as “the” SDLC. |
| Secure use of AI coding | OWASP Top 10 for LLM | Security risks when LLMs touch design, code, or data—baseline for agentic / AI-assisted workflows in this blueprint. |
Related blueprint guides
- Spec-driven development — durable specs in the repo (acceptance criteria, IDs) before large agent-driven edits; complements this guide. Handbook:
../docs/spec-driven.html. - Roles & archetypes — human accountability vs Contributor identity when agents commit.
- Ceremonies — foundation intents vs methodology forks; where humans stay in the loop for planning and acceptance.
- Agile (umbrella)
- Scrum · Kanban · XP · Phased delivery
- Execution layer (optional): containerized recipes and
agents/workspace — see Agents blueprint — structure & layers and handbook../docs/agents.html.
Canonical source
Edit https://github.com/autowww/blueprints/blob/main/sdlc/methodologies/agentic-sdlc.md first; regenerate with docs/build-handbook.py.