78 lines
6.4 KiB
Plaintext
78 lines
6.4 KiB
Plaintext
---
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id: roadmap
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title: Roadmap
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description: Build proven skills in human and AI Agent work, starting with Delivery, as the foundation for the future autonomous SDLC.
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hide_table_of_contents: true
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---
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import ArchifyDiagram from '@site/src/components/ArchifyDiagram';
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<div className="strategy-content">
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## Two stages toward the long-term Target
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<ArchifyDiagram name="strategy-roadmap" title="Adopt skills → Future Autonomous Agent" story />
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**Humans direct, guide and review throughout.** Stage 1 standardizes skills in human + AI Agent work across the SDLC. Proven skills, measurements and working practices become the foundation for Stage 2: the future Autonomous Agent in [Target](./target-operating-model.mdx).
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This is the broad progression, not a month-by-month schedule. The [6 Months Target](./six-months-target.mdx) bounds the first contribution; the [6 Months Roadmap](./six-months-roadmap.mdx) owns the concrete action plan.
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Target operating model—not deployed autonomy, an agreed schedule or approved KPI thresholds. Stage 1 is useful in its own right; Stage 2 requires a separately justified change in authority, not graduation for using enough skills.
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## Stage 1: Delivery first
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<ArchifyDiagram name="strategy-roadmap-adoption" title="Delivery first → Other SDLC phases, order undecided" story />
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Start with Delivery, then extend skills adoption to the remaining SDLC phases. Their order is not yet decided. **Delivery keeps improving as adoption expands.**
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## Improve within each phase
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<ArchifyDiagram name="strategy-roadmap-loop" title="Each phase: understand → baseline → topology → use → collect → reassess → repeat" story />
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Repeat this loop within every phase—not as six global roadmap stages. Skills topology defines **what skills exist, how they relate, when they are used and how they work together**, including handoffs—not just a catalogue.
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## Delivery in practice
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**Understand the engineering path first:** codebase structure and existing principles → system-logic source → tests → builds and packaging → deployments. Inspect actual practices, tooling, controls and problems with the people doing the work, rather than assuming the documented process is the real one.
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**Establish the comparison, then choose the intervention.** Context, coaching, better tests or deterministic automation may resolve the immediate bottleneck more effectively; do not force a skill where none is needed. Select relevant metrics/KPIs and a baseline for comparable work before changing it. [Measurement](./measurement/index.mdx) supplies optional definitions; [Baseline](./baseline.mdx) records the actual starting position or gaps. Connect skills to the engineering path, with clear triggers, dependencies and acceptance checks; [Quality](./skills-quality.mdx) qualifies the candidate before bounded real use.
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**Use, collect, compare, repeat.** A human works with an AI Agent on real changes. Collect results, friction and feedback alongside quality, cost and verification effort. Compare with the baseline to understand the new current situation; retain, improve, stop or propose expansion. Changed revisions return to qualification; changed workflows need a fresh comparison.
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Follow the **illustrative Delivery workflow** in [Stage 01](./stage-01-skills-adoption.mdx): prepare a bounded code change and regression tests, then hand the exact artifact to independent review. Its dependency diagram shows what passes between activities and who decides whether work can progress. Governance and Measurement support the whole loop; they are not one-time gates.
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<details>
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<summary>Readiness questions behind the roadmap</summary>
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These are readiness questions, not additional roadmap stages. Stage 01's topic pages define the recommended decisions and evidence within existing teams. Designate accountable people and agree workflow-specific acceptance criteria before evaluating; this strategy does not appoint approvers or approve thresholds.
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### Understand the current situation
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Sample representative repositories and changes. Retain repository revisions, tests and CI reliability, maintainability, technical debt and security findings. Trace ownership, review and release practices, handoffs, rework and bottlenecks. Keep baseline findings, a process map and prioritized gaps; hold selection if the sample cannot explain how work is delivered and accepted.
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### Prepare safe, measurable human + AI work
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Inventory actual client hosts/builds, provider/data terms, authority and event availability. Confirm accountable people and independent reviewers, forbidden actions, data/retention decisions, recovery and a reproducible baseline. Hold if accountability, lawful collection or critical acceptance checks remain unresolved.
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Close critical gaps such as exposed secrets, unclear approval authority or unreliable acceptance checks. Broader refactoring and non-critical debt can run alongside the selected work. Stage 1 introduces no new privileged autonomous actions.
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### Engineer and evaluate connected skills
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Use a small registry, immutable skill contracts, qualified client adapters, evaluations and coaching. Verify the measurement calculations with synthetic fixtures before interpreting real outcomes. Require mandatory safety checks, independent task acceptance, reconciled source coverage and user feedback. Hold or narrow scope if quality or coverage is inadequate; usage alone does not justify expansion.
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### Extend adoption without stopping improvement
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Prove canary updates, effective-version readback, version locking, exception expiry, stale/offline handling, rollback and revocation. Hold if effective versions or controls cannot be verified or the catalog cannot be supported.
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Use evidence to choose the next SDLC phase; no second or third phase is assigned here. Continue the improvement loop in Delivery and every phase already using skills.
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### Prepare for the future Autonomous Agent
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Stage 2 builds on proven skills, measurements and working practices—not permission inferred from adoption alone. [Beyond 6 Months](./stage-02-autonomous-agent.mdx) owns the separate authority/readiness decision. Recommended controls include exact-artifact approvals, idempotency, duplicate/outage recovery, separation of duties, audit and kill-switch drills. Prove human takeover; never infer permission from missing evidence. Keep sensitive decisions human. Changed client, provider, data or authority boundaries reopen the relevant checks.
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</details>
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**Next:** [6 Months Target →](./six-months-target.mdx)
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</div>
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