AI-native software development, engineered — not improvised.
PhaiAI builds the domain-focused agents, agentic workflows and security guardrails that turn the Agentic Software Development Lifecycle from a conference slide into something your teams can actually operate.
- ASDLC
- Lifecycle design, paths and gates for agent-driven delivery
- Agents
- Packaged, governed, installable on any agentic harness
- Security
- Agentic threat surfaces closed at design time, enforced at runtime
Putting an AI coding tool on top of a human SDLC does not give you an ASDLC.
It gives you a faster keyboard. The lifecycle only changes when the triggers, the handoffs and the gates change with it — and that is platform work, not procurement.
The uncomfortable part
“We rolled out an AI coding tool.”
You changed the editor. The lifecycle still assumes a human initiates, reviews and deploys every stage, so the gain stops at autocomplete speed.
“Our agents generate hundreds of PRs.”
Without a validation loop, that is defect throughput. Review bandwidth becomes the bottleneck the moment generation stops being one.
“The model just isn't good enough yet.”
Usually it is the onboarding. An agent dropped into documentation written for humans and guardrails held as tribal knowledge behaves exactly like a brilliant hire nobody briefed.
The loop is the unit of delivery, not the pull request.
In an agentic lifecycle, a first-pass failure is not a defect — it is an iteration. The platform's job is to make repeated runs against error-updated context converge on a correct result, then stop.
Four practices, one substrate.
Agents, the platform they run on, the controls that make them safe to run, and the cost discipline that keeps them running. Taking any one of these without the others is how AI programmes stall in the pilot phase.
Practice 01
ASDLC agents & agentic workflows
Five domain-focused agents — Solution, Development, Testing, Deployment, BugFix — sequenced by path definitions rather than by hope. Each one ships as a versioned package with a persona, rules, skills and a manifest.
- Domain-focused, not general-purpose
- Wired into a Domain Context Engine by default
- Distributed through Agyle on the apm manifest
Practice 02
Agentic Engineering Platform
The substrate that makes agents operable: identity, sandboxed workspaces, context packaging, evaluation as infrastructure, and observability that triggers work instead of merely describing it.
- Built on your existing IDP, not instead of it
- Domain-driven boundaries and ubiquitous language
- Agent Infrastructure as Code, from day one
Practice 03
Agentic security engineering
Agency introduces surfaces the LLM threat model does not cover. We design for them at design time — memory access policy, safe-failure states, scoped identity — and enforce them at runtime through a Guardian.
- OWASP GenAI Security Project guidelines, applied
- Agent Control Standard on the wire
- AgBOM inventory for every agent you run
Practice 04
AI cost engineering
Token prices collapsed and AI budgets grew anyway, because an agent is a loop that re-reads its context on every turn. We compress that context, layer the memory behind it, and budget the path on cost per successful outcome.
- Context compressors with an eval attached
- User, team and domain memory layers
- Cost budgets enforced in the path definition
Domain-focused agents, not general-purpose assistants.
A general agent knows how software is usually written. A domain-focused agent knows how yours is — its patterns, its regulatory text, its ADRs, and the three incidents nobody wants to repeat. That difference lives in the Domain Context Engine underneath.
Each agent ships with a persona, rules, skills and an AGENTS.md manifest.
Every engagement starts by locating you on the curve.
Most enterprises are at level 1 and are being sold level 3. Attempting level 3 on a level 1 substrate produces agents that run faster than the guardrails around them — which is not ambition, it is exposure.
Built on open standards, credited openly.
We would rather be measured against a published specification than against our own marketing. Every agent we ship is governed by the Agent Control Standard and developed against OWASP GenAI Security Project guidance.
The full bibliography — including the ASDLC and Agentic Engineering Platform research we build on — is on the research page.
OWASP GenAI Security Project
Agentic developer guidelines, applied across design, build, deploy and run
Agent Control Standard
Runtime hooks, dispositions and audit trail for every agent we ship
One domain. One value stream. One governed loop that works.
We do not open with a transformation programme. We pick a bounded context, stand up a Domain Context Engine and one hybrid path end to end, prove it against your own definition of done, and only then talk about scale.