Install the lifecycle, not another tool
Your review gates were designed for human authorship. The moment agents produce the majority of the diff, code review becomes either rubber-stamping or the bottleneck that erases the speed you bought AI for. Neither is acceptable.
ADLC Platform Enablement installs the lifecycle itself — agent CI/CD with worktree isolation, policy-as-code release gates, cost governors and drift telemetry — then hands it to your engineers with the runbooks to operate it.
Why teams bring this to us
Follow-the-sun operations
What you own at handover: an agent CI/CD pipeline with adversarial verification, a policy-as-code gate stack with fail-closed defaults, a cost governor enforcing per-task and per-tenant budgets, a tamper-evident audit chain recording model, prompt hash, tokens, cost and approver for every call, an eval harness with golden-set parity tests and drift detection, plus architecture decision records behind every choice.
Ten to twelve weeks, fixed scope, working software in your environment every two weeks.
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Gartner expects 40% of agent projects to be cancelled by 2027.
Not because the models underperform. The three reasons cited are runaway cost, governance gaps, and no defensible ROI — every one of them an engineering and process failure rather than a modelling one.
Your review gates were designed for human authorship. They break the moment agents produce most of the diff.
- Review becomes theatre. Approvers cannot meaningfully assess the volume, so sign-off turns into a formality your auditors will eventually test.
- Spend stops being attributable. The invoice arrives as one number. Nobody can tie a dollar to a decision, so nobody can cut cost without cutting capability.
- The record does not survive scrutiny. Asked why the system did something six months ago, the honest answer is a log search and an inference.
- Agents drift outside their mandate. Broad tool access granted early becomes an undetectable blast radius later.
- Remove the gates. Velocity rises, control disappears. This is the path most teams take by default, usually without deciding to.
- Keep the gates as they are. Governance holds, and the throughput you bought AI for evaporates into a review queue.
- Neither is acceptable for a system that touches revenue, customer data, or a regulated process.
Keep every gate. Make each one machine-enforceable.
The Agentic Development Lifecycle is not a methodology deck. It is a set of running components that sit in your pipeline and refuse to let unverified work through.
Throughput rises. Governance does not degrade. Both become measurable, which is the part most AI programmes cannot currently claim.
Intent
Requirements captured as executable contracts with acceptance criteria, not tickets an agent has to interpret.
Plan
Decomposition with token budget, blast radius and escalation ladder declared before any agent starts.
Generate
Tiered agent workforce in isolated worktrees. Model class matched to measured task complexity.
Verify
Adversarial review agents. A change ships only if the case against it fails.
Release
Policy-as-code gates, signed provenance, rollback triggers declared before deploy rather than during an incident.
Observe
Drift, decay, cost and behaviour tracked as first-class SLOs with automatic demotion.
What you own at handover.
Ten to twelve weeks, fixed scope, working software in your environment every two weeks. Nothing here is a dependency on us continuing.
| Artefact | Contents | Phase |
|---|---|---|
| Agent CI/CD | Worktree isolation, parallel execution, adversarial verification stage, merge policy that cannot be bypassed by an agent | Wks 1–4 |
| Policy gate stack | Tool allowlists, approval gates on irreversible actions, fail-closed defaults, capability stripped at the protocol layer rather than discouraged in a prompt | Wks 3–6 |
| Cost governor | Per-task, per-agent and per-tenant budgets that throw rather than silently degrade; tiered model router; per-call ledger | Wks 5–8 |
| Audit chain | Tamper-evident event log covering every state transition, gate verdict and model call | Wks 5–8 |
| Eval harness | Golden-set parity tests, drift and decay detection, false-positive guards, regression suite wired into the pipeline | Wks 7–10 |
| Runbooks & ADRs | Operating procedures, escalation ladders, and a written decision record behind every architectural choice | Throughout |
We ran this on ourselves before selling it.
AAQuant is our reference implementation — an autonomous research organisation operating unattended under exactly the lifecycle described above. We publish its operating metrics rather than describing them.