AI software delivery
Product intent in, working and observed software out, with agents doing the authoring rather than assisting it.
Delivery capacity is the constraint on everything else #
Almost every digital ambition is rate-limited by the same thing: how fast an organisation can specify, build, review, release and operate software. Hiring moves that limit slowly and expensively.
AI coding assistants help an individual engineer type faster, but the pipeline around them is unchanged. Somebody still writes the specification, decomposes the plan, reviews the code, shepherds the merge, watches the deployment and files the defect. The bottleneck moves along the line; it does not disappear.
The familiar symptoms
- A backlog measured in quarters, and a roadmap that is really a queue.
- Assistants that raise the volume of code without raising confidence in it.
- Quality that depends on who happened to review the change.
- Nobody able to say whether what was tested is exactly what shipped.
Agents are interchangeable and disposable. The plan, the dependency graph, the quality gates and the event-sourced release path are the durable parts.
A standing software factory, not a coding assistant #
The Agent² Software Factory is an end-to-end pipeline that turns product intent into shipped software with no human authoring. People express the outcomes they want and grant approval at the points that need judgement. Agents perform the design, architecture, planning, implementation and delivery.
Quality is a property of the pipeline rather than of any one agent. Every stage is gated by automated, fail-closed verification: static validation, tests, adversarial review, then live tests against actually-deployed services. A green result is required before anything is promoted.
It is the same machinery Agentycs uses to build Agentycs, and it feeds its own production signals back in as new work.
- Agent² Software Factory
- Intake, design, planning, the agent workforce, the quality gates, integration and GitOps release.
- Application Framework
- The declarative app framework agents author against, so they express behaviour instead of plumbing.
- Platform Foundation
- Durable workflows, the runtime the delivered software lands on, and the observability the loop feeds from.
- Apex
- The accelerators the agent workforce runs on, so your delivery pipeline is not metered by an external provider.
How intent becomes shipped software #
Each stage has a machine-checkable exit condition. Nothing advances on an assurance.
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Intent, resolved into a specification
Intake agents interview the requester on the genuine product and architectural forks — the decisions only a person can make — and skip the boilerplate, until the brief is unambiguous and decidable.
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Competing designs, adversarially judged
Independent agents propose risk-first, simplicity-first and reuse-first approaches. A judge panel scores them for correctness, security, fit to established patterns and blast radius, then synthesises the strongest design and records why.
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A plan that cannot be wrong-ordered
Work is decomposed into a dependency graph with cycle detection and forward-only ordering, validated on every write. Readiness is computed and provable rather than guessed in a planning meeting.
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A fleet that builds in parallel
Each agent works in its own isolated workspace and reproducible environment, so many changes progress at once without interfering. Its whole session — turns, tool calls, model, tokens, cost, outcome — becomes a queryable timeline.
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Boilerplate generated, not written
New applications, typed contracts and the REST, tool and live-query surfaces over them are generated from a single declaration, so every protocol stays in lockstep automatically.
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Layered, fail-closed gates
Repository validation, unit and integration tests, adversarial code and security review that defaults to refute-and-verify, then declared live-test suites run against real deployments with request, trace, log and metric diagnostics attached.
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Release you can reverse
Immutable artefacts are promoted between environments, so what was tested is exactly what ships. Deployment waits on real convergence and health, with canary rollout and automatic rollback on any regression.
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A loop that closes on itself
Production alerts, live-test regressions and drift between intended and actual state become new structured work automatically. Captured sessions are mined to improve the process the factory runs next time.
Where the factory runs #
The pipeline runs on your own infrastructure, which is what allows it to build software for environments that never touch the public internet.
Bring us something you have not had capacity to build
The clearest way to evaluate autonomous delivery is a real piece of work with real acceptance criteria. We will walk you through what the factory produced and every gate it had to pass.