Agentic Development Life Cycle

Ship software with agents in the loop and humans in control.

ADLC turns a product intent into planned work, isolated implementation, verification evidence, review decisions, and release learning.

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$ adlc run "reduce checkout latency"
intent       captured with acceptance criteria
planner      split into bounded tickets
coder        isolated workspace created
tester       regression and smoke evidence attached
review       human gate pending
release      ready when approved

Process

A lifecycle built for controlled agent work.

Frame

Capture intent, constraints, source context, and policy boundaries before any agent acts.

Plan

Convert goals into sequenced tickets with explicit assumptions, dependencies, and approval gates.

Build

Run implementation in isolated workspaces where credentials stay behind controlled services.

Verify

Attach tests, traces, diffs, review notes, and release evidence to the work item.

Evidence

Designed for readable state, not agent theatrics.

Every stage produces durable artifacts: plans, task context, run logs, test output, review decisions, and operational feedback. Teams can inspect what happened without reverse-engineering a chat transcript.

Governance: policy-aware handoffs and human approval points.
Isolation: per-run workspaces with proxied egress and no raw provider secrets.
Continuity: learning loops that feed future planning and support workflows.

Open the current AI Fleet workspace.

The public ADLC page is separate from the application. The workspace route remains unchanged.

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