01
Coordinate specialised agents, schedule work, resolve dependencies, and enforce execution policies across parallel tasks.
Software Factory ServiceThe Platform That Lets
The Platform That Lets
AI Agents Build Software At Scale
A software factory provides the orchestration, context, validation, governance, and infrastructure that let AI agents work safely on the same codebase at scale.
Trusted by engineering teams at
The Challenge
As organisations adopt
AI coding agents, the challenge shifts from generating code to coordinating it. Multiple agents working across the same codebase require shared context, engineering standards, validation, governance, and controlled access. A software factory provides the system that makes this possible.
Core Components
The Six Core Software Factory Capabilities
02
Execute every task in an isolated, reproducible environment with identical tooling, dependencies, and production-like context.
03
Provide shared code, architectural decisions, engineering standards, and organisational knowledge for consistent agent behaviour.
04
Verify every change through automated tests, evaluations, security scans, policy checks, and quality gates before merge.
05
Control permissions, credentials, tool access, and audit trails so every agent action is secure and traceable.
06
Continuously improve agents using production feedback, evaluation results, reusable context, and organisational knowledge.
01
Orchestration
Coordinate specialised agents, schedule work, resolve dependencies, and enforce execution policies across parallel tasks.
02
Isolated Environments
Execute every task in an isolated, reproducible environment with identical tooling, dependencies, and production-like context.
03
Context & Memory
Provide shared code, architectural decisions, engineering standards, and organisational knowledge for consistent agent behaviour.
04
Validation
Verify every change through automated tests, evaluations, security scans, policy checks, and quality gates before merge.
05
Governance
Control permissions, credentials, tool access, and audit trails so every agent action is secure and traceable.
06
Learning
Continuously improve agents using production feedback, evaluation results, reusable context, and organisational knowledge.
The throughput comes from the system, not from any single AI model
Execution Model
How A Software Factory Operates
- 01
Specification
Engineers define the outcome, constraints, and acceptance criteria. Clear specifications replace prompts as the primary interface between humans and AI agents.
- 02
Agent Execution
An orchestrated swarm of specialised agents plans, implements, tests, and reviews the change, each responsible for a specific part of the workflow.
- 03
Verification
Every change is validated through automated tests, evaluations, security scans, and policy checks. Verification—not human review—determines whether work is ready to merge.
- 04
Integration
Verified changes are merged into the shared codebase, preserving consistency across teams, applications, and parallel agent workflows.
- 05
Continuous Delivery
Changes are progressively deployed, monitored, and fed back into the next specification, creating a continuous engineering loop that improves over time.
Engineering Leadership, Every Two Weeks
Practical writing on building AI-native engineering — software factories, agent workflows, verification, and governance — written for engineering leaders.
Bi-weekly. No spam, unsubscribe anytime.
- 01
Assessment
Identify the bottlenecks that limit AI adoption today.
Current architecture, delivery process, engineering practices, and Factory MVP scope, written up as the report you can sample.
- 02
Intent
Standardise how work is specified.
Executable specifications, acceptance criteria, and traceable requirements become the foundation.
- 03
Context
Capture engineering knowledge once.
Shared documentation, architecture, standards, and reusable engineering context for every agent.
- 04
Verification
Automate trust before scaling AI.
Tests, evaluations, security checks, and governance verify every change automatically.
- 05
Factory MVP
Prove the factory on one production workflow.
Deliver one vertical slice from specification to deployment using the complete factory.
- 06
Rollout
Expand the factory across teams and applications.
Onboard repositories, products, and engineering teams onto the same platform.
Progressive Autonomy
AI autonomy increases as verification improves
Every engineering workflow starts with humans approving each change. As more of that workflow becomes automatically verified—through tests, policies, security checks, and evaluations—the factory can safely delegate more work to AI.
AI Autonomy by Verification Coverage
Stage 1
Lights On
Human in loop
Low AI autonomyHigh AI autonomy0%Verification Coverage100%Every change requires human approval. AI assists implementation.
Stage 2
Dimming
Human on loop
0%Verification Coverage100%Most changes verify automatically. Humans review exceptions.
Stage 3
Lights Out by Exception
Human above loop
0%Verification Coverage100%Verified changes deploy autonomously. Humans intervene only for uncertainty or policy violations.
The chart shows how each workflow progresses from human-led to autonomous delivery.
FAQs
Common Questions
Coding assistants improve individual developers. A software factory standardises how AI is used across teams through shared context, verification, governance, and orchestration.