🎉 Initial commit: Integrate Agentic SDLC Starter Kit

Establish the Meeting Secretary repository with foundational files for
an agentic-first development workflow. This includes guiding principles
for teams working with AI coding assistants, a reference for Red Hat's
AI-assisted contribution policy, and documentation on the agentic-first
software development philosophy.

These foundational files provide common guardrails and conventions for
Fedora contributors collaborating on prompt engineering and AI/LLM
tooling in a public, community-driven context.

Assisted-by: Claude Opus 4.6 (1M context)
Signed-off-by: Justin Wheeler <jwheel@redhat.com>
This commit is contained in:
Justin Wheeler 2026-04-15 19:21:41 -04:00
commit 2f9c810cda
Signed by: jflory7
GPG key ID: 7748B15FA8FA4C7E
4 changed files with 607 additions and 0 deletions

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# Red Hat — AI-assisted development (project reference)
**Canonical policy**: [Guidelines for Responsible Use of AI Code Assistants](https://source.redhat.com/projects_and_programs/ai/wiki/code_assistants_guidelines_for_responsible_use_of_ai_code_assistants) (supplement to Red Hat's Policy on the Use of AI Technology).
If this file conflicts with the wiki, **the wiki wins**—update this file.
## Summary — seven guidelines
1. **Human in the loop**: Treat AI output as suggestions; review, test, and modify before integrating.
2. **Approved tools only**: Use tools and models approved through Red Hat's AI assessment (and Buy@RH where applicable).
3. **No sensitive data in prompts**: Do not input confidential, personal, or proprietary/customer data (passwords, API keys, credentials, PII). Sanitize snippets; prefer synthetic data.
4. **Matching / third-party similarity**: If the tool shows "match" information for third-party code, verify before use. AI output may resemble copyrighted training data—investigate license implications when unsure.
5. **Upstream policies**: Before contributing to open source, comply with **that project's** AI rules (some prohibit or restrict AI-assisted contributions).
6. **Mark substantial AI-generated portions**: Use commit trailers and/or comments as below.
7. **Copyright and license notices**: Follow team practice for file headers; substantial generation with little human creative input especially needs clear marking.
## Attribution — commits and PRs
Use **one** of these in the commit message footer (or clearly in the PR/MR description):
```text
Assisted-by: <name of code assistant> <model name> <model version>
Generated-by: <name of code assistant> <model name> <model version>
```
- **Assisted-by**: You directed the work and edited meaningfully (default for typical use).
- **Generated-by**: A substantial portion was generated with minimal human edit (e.g. full file scaffold).
Prefer `Assisted-by:` or `Generated-by:` over `Co-Authored-By:` for AI tools — AI is not a co-author in the traditional sense, and `Co-Authored-By:` may have CLA/stats implications in some projects.
### Source file comments (when needed)
```text
// Generated by <name of code assistant> <model name> <model version>
// Assisted by <name of code assistant> <model name> <model version>
```
Place at top of file or start of the relevant block, per project conventions.
### Quick decision
- Meaningful human direction and edits → **Assisted-by**
- Large generated chunk, little change → **Generated-by**
- If unsure → **Assisted-by**
## Proactive warnings (for AI assistants)
- **Secrets / PII / proprietary data**: If the user pastes or asks to use them in prompts, warn and suggest redaction or synthetic data.
- **Upstream**: If the task mentions kernel or a known restrictive upstream, warn to verify that project's AI contribution policy.
- **Large generated blocks**: Remind to mark in commit and optionally in-file comment per the attribution rules above.
## EU / works council
Some geographies require **works council** or local review before adopting tooling. Follow your management chain if unsure.
## Upstream always
Check target project policy **before** submitting AI-assisted patches (kernel and other communities may have strict rules).

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# Agentic-first SDLC — team playbook (short)
**Audience**: Any team using AI coding agents (Cursor, Claude Code, etc.).
**Goal**: Better **outcomes** (efficiency or productivity) with **quality**—not maximizing "% of lines from AI."
## 1. Principles
1. **Agent as primary author, human as director** — You specify intent, context, and acceptance; you review and own the result.
2. **Context is infrastructure**`AGENTS.md`, README, rules, and clear naming reduce rework; stale context is debt.
3. **Human accountability** — Shipping code is always a human decision; "the agent did it" is not a root cause.
4. **Efficiency or productivity** — Same work with fewer people, or more value with the same team—pick the lens that fits your charter.
## 2. Context management
### Repository
- Maintain **`AGENTS.md`** (architecture, layout, conventions, pitfalls).
- **README**: how to build, test, run—not marketing fluff.
- Document naming patterns and error-handling philosophy if non-obvious.
### Issues / tickets
Write for someone who does not know the repo:
- **Goal** (outcome), **acceptance criteria** (testable), **files/modules**, **constraints**, **links** to prior art.
### Sessions
- State scope, point to files, list **non-negotiables** (no API break, no new deps, etc.).
- If the session drifts, **reset** with a fresh summary or new session.
## 3. Development loop
```
Specify intent → Agent proposes changes → Human reviews → Refine or accept → Repeat
```
**Agents tend to be strong at**: boilerplate, refactors, tests, docs, localized bug fixes with reproducers.
**Humans should lead**: novel architecture, security-sensitive paths, performance-critical design, ambiguous product trade-offs.
## 4. Code review (what changes)
Focus less on typos; focus more on:
- **Semantic correctness** and edge cases
- **Hallucinated APIs** / wrong flags / imaginary config
- **Over-engineering** and missing error handling
- **Consistency** with existing patterns
**Agent-assisted review** can summarize diffs or find inconsistencies—it **supplements**, not replaces, human review.
## 5. Testing
- Use agents to write tests, then **critically** review: behavior vs implementation coupling, edge cases, maintainability.
- **Test-first pattern**: agree cases → failing tests → implement → review.
## 6. CI/CD and compliance
- **No bypass**: agent-produced code uses the same CI, scans, and gates.
- **Attribution and data handling**: see `REDHAT.md`.
## 7. Metrics (optional)
Useful **optional** metrics: velocity (after re-baselining points), PR throughput, time-to-merge, defect rate, review time.
**Avoid** optimizing raw "% AI-generated" or LOC—those are poor proxies for value.
## 8. Alignment reminders
Organizational **principles**, **tenets**, and **operating-model maxims** (including how they relate to guardrails and hierarchy) live in **[docs/principles.md](principles.md)**. Skim them when onboarding or changing team process.

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# Guiding principles and tenets — agentic SDLC
**Audience**: Teams adopting an agentic-first SDLC.
**Relationship**: This doc is the **organizational** “why” and operating model. For day-to-day workflow (context, review, testing), see [agentic-sdlc.md](agentic-sdlc.md).
## Core ideas
These ideas underpin the principles below:
- **Built-in, not bolted on** — Requirements, checks, and outputs are encoded in the workflow (not only reviewed after the fact).
- **API contract between agents** — Every team encodes what they require, what they check, and what they produce—as `AGENTS.md` files, skills, or rules—creating a contract other agents can rely on.
- **Guardrails compose hierarchically** — Org → product → team → repo. Every agent inherits the full chain. By design, guardrails are **non-bypassable** where policy requires it.
- **Humans define the problem; agents execute within guardrails** — Taste and judgment live at intent and design; implementation and validation scale through agents.
- **Role evolution, not replacement** — The goal is output and leverage per person; roles shift toward architecture, encoding expertise, and judgment—not wholesale substitution.
## Principles
Strategic direction aligned with engineering leadership and architectural guardrails. Goals should survive vendor and model changes.
1. **Outcomes, not tools** — Optimize for durable goals (quality, velocity, risk reduction), not a specific IDE, model, or vendor. Tooling changes; outcomes stay.
2. **Rethink workflows through agents** — Redesign how work flows (concurrent validation, encoded checks, atomic change units), not only speed up the old serial gates.
3. **Guardrails are inherited, not inspected** — Expectations live in rules, skills, and repo context so they apply consistently; “catch it in review only” is insufficient for scale.
4. **Measure at the workflow level** — Prefer cycle time, throughput, defect rate, and leading quality indicators over raw “% AI-generated” or line counts.
5. **All-in product scope** — Treat the whole product surface (security, docs, UX consistency, performance, support signals) as part of delivery scope, not optional add-ons after code merges.
6. **Upstream commitment** — Respect upstream and open-source policy: verify contribution rules, attribution, and license implications before shipping agent-assisted changes upstream (see [REDHAT.md](../REDHAT.md)).
## Tenets
How we apply the framework without prescribing a single toolchain:
1. **Non-prescriptive framework** — Teams choose assistants, CI, and practices; the model is defined by outcomes and encoded guardrails, not one stack.
2. **Success through outcomes (efficiency or productivity)** — Success is either the same work with fewer bottlenecks, or more value with the same team—pick the lens that matches the charter.
3. **Quality and leading indicators** — Define quality explicitly (tests, security bars, docs, UX, performance) and track signals that predict defects and rework—not vanity metrics.
4. **Upstream always** — Upstream policy and community norms are part of the definition of done for contributions that leave the orgs boundary.
## Operating-model maxims
Rules for changing how we work **inside** the agentic model. Add to this list as the org learns.
- **Agents first for process change** — To change process, deliverables, or quality bars, **change agents** (context, skills, rules, automation) so behavior matches intent **before** relying on tribal reminders or manual gates alone.
## Related docs
- [agentic-sdlc.md](agentic-sdlc.md) — Playbook: context, development loop, persona lifecycle, review, testing, metrics
- [../AGENTS.md](../AGENTS.md) — Repo-level context for assistants
- [../REDHAT.md](../REDHAT.md) — Red Hat AI policy and attribution