METHODOLOGY · PILLAR 03 / 05 · GENERATION

Engineering Agents

Specialised AI systems that assist across the engineering lifecycle. Engineering agents work within the constraints defined by foundations, specifications, and governance, extending human capability without replacing human judgement.

The Engineering Agent Classes

Code Generation Agents

Purpose: Create new code aligned with architectural patterns.

They generate:

→services
→modules
→handlers
→integrations
→domain entities
→event producers/consumers
→configuration and scaffolding

Always using:

→approved templates
→naming rules
→boundaries
→DTOs & schemas
→error strategies
→observability footprints

Never improvising architecture.

Refactoring & Modernisation Agents

Purpose: Improve and update existing code within constraints.

Capabilities:

→clean up long-lived services
→extract modules
→apply patterns
→update naming
→restructure directories
→upgrade libraries safely
→fix drift
→align legacy code to modern patterns

These agents generate reversible refactorings with diffs, summaries, and rollback instructions.

Test Generation Agents

Purpose: Increase coverage and strengthen correctness.

Capabilities:

→unit tests
→integration tests
→property-based tests
→contract tests
→scenario simulations
→regression suites

Tests are derived from actual system behaviour and telemetry, not guesswork.

Documentation & Design Agents

Purpose: Keep the system continuously explainable.

Capabilities:

→ADRs
→component summaries
→architecture notes
→domain diagrams
→change logs
→dependency maps
→API documentation
→onboarding guides

Documentation becomes a living artifact, not a forgotten folder.

Domain Reasoning Agents

Purpose: Understand and enforce domain logic.

Capabilities:

→rules
→policies
→domain terms
→state transitions
→decision boundaries
→human-in-the-loop checkpoints

These agents ensure that generated code respects the domain, not just compiles.

Telemetry & Runtime Analysis Agents

Purpose: Read system behaviour, not source code.

Capabilities:

→latency patterns
→throughput
→drift
→error rates
→hot paths
→retry storms
→cost trends
→health degradation

They analyse:

→latency patterns
→throughput
→drift
→error rates
→hot paths
→retry storms
→cost trends
→health degradation

Agents can propose:

→performance fixes
→cleanup tasks
→indexing changes
→architectural improvements
→DDD boundary repairs

This brings "runtime intelligence" into engineering.

Workflow & CI/CD Agents

Purpose: Automate engineering flows in a governed pipeline.

Capabilities:

→PR generation
→static analysis
→risk scoring
→release note generation
→merge recommendations
→sanity checks
→pipeline enhancements
→environment preparation

These agents keep delivery fast, consistent, and safe.

How Agents Operate in AI-Native Engineering

Scoped Roles

Each agent has a precise, limited responsibility.

Controlled Access

Strictly permissioned operations with audit trails.

Architectural Enforcement

Patterns, templates, boundaries, enforced automatically.

Validation Gates

Human review + policy enforcement + schema checks.

Telemetry Feedback

Agents continuously learn from runtime signals.

Reversibility

Every modification is undoable, traceable, and explainable.

Continue exploring

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