The problem
The product portfolio has expanded dramatically in recent years, and the pace of growth is accelerating. Traditional software development cannot accommodate this scaling, and incremental improvements to the existing process are not enough.
Requirements translation
Product requirements are written for positioning and decision-making, not for agent consumption. Engineering must translate abstract requirements into precise, agent-readable specs, creating friction at the start of every feature cycle.
Development and QE handover
Testing knowledge lives in the heads of QE engineers, not in structured documents. Test generation depends on manual interpretation of requirements, introducing delays and coverage gaps with every release.
No AI-native harness
Coding agents are powerful but used in isolation. There is no systematic harness connecting spec generation, code execution, testing, review, documentation, and deployment into a single coordinated loop.
Process bottlenecks
Review cycles, human-bottlenecked CI pipelines, and documentation treated as an afterthought compress the velocity that AI coding delivers. The agent loop is fast; the surrounding process is not.
The harness
The AIDLC harness is an assembly line for software. Product, Development, and QE collaborate once to write a complete spec. Agents then drive every subsequent phase, with humans verifying at defined gates.
North Star goal: Fully autonomous software development for the majority of Couchbase components. Humans set direction and verify gates; agents drive execution.
The process
Collaborative Spec Development
Product Manager, Development SME, QE SMEProduct managers contribute the WHAT, writing requirements in a format that agents can consume directly. Development SMEs add the HOW, detailing architecture, design decisions, and technical constraints. QE SMEs define the TEST, specifying scenarios, corner cases, and test types. Together, these form a single complete spec pushed to the Spec Registry. This document is the authoritative input for everything that follows.
Autonomous Coding and Testing
Orchestration agent, coding agents, testing agents, development gate, QE gateThe orchestrator feeds the spec to a coding agent, which generates the feature as a pull request. A developer reviews and approves before the orchestrator triggers test generation. A QE engineer reviews the generated tests and approves. The orchestrator then deploys sandbox environments and runs all tests automatically. Failures are sent back to the coding agent for a fix attempt. If the second attempt also fails, a Human in the Loop (HIL) alert fires. Once tests pass, an AI review agent inspects the pull request before it moves to human review. The orchestrator reruns the full test suite before merging.
Documentation and Deployment
Documentation agent, Docs engineer, Product Manager, Engineering gateOnce code is merged, the orchestrator initiates documentation generation. Drawing on the PRD, design documents, and documentation skill files, the documentation agent produces both internal and public-facing drafts. A human gate covering the Docs engineer, Product Manager, and Engineering reviews and approves before publication. This gate remains permanent: hallucination in documentation cannot be caught deterministically the way test failures can. Deployment follows after documentation approval.
Orchestration
A single orchestration agent drives the entire loop. It coordinates a fleet of specialized agents, each equipped with component-specific skill files that encode domain expertise for the area being developed.
Adaptive retry
When code generation or test execution fails, the failure details are automatically fed back to the responsible agent for a second attempt. A Human in the Loop (HIL) alert fires only if the second attempt also fails, keeping the loop moving without unnecessary interruption.
Skill-backed agents
Every agent in the harness is backed by component-specific skill files sourced from the Skill Registry. These files encode engineering knowledge, patterns, and quality standards for the component being modified, guiding the agent toward correct and idiomatic output.
Measured gates
The orchestrator tracks the number of human review comments generated per pull request. This metric is the primary indicator of progress toward full autonomy. As the count approaches zero, human review gates can be progressively removed from the pipeline.
Continuous context
The Semantic Knowledge Graph provides real-time codebase context to every agent on every invocation. Agents query it for relevant code summaries, design decisions, dependency relationships, and historical engineering knowledge before taking action.
Semantic Knowledge Graph
Intelligence layer
The Semantic Knowledge Graph (SKG) is a data context engine that stores and connects all engineering knowledge. Every agent in the harness queries it throughout execution: during spec generation, code writing, test authoring, review, and documentation.
The Engineering Context MCP exposes the SKG as a set of tools that agents call over HTTP, giving any compatible agent access to the full knowledge graph scoped to a project and user.
Evaluation systems
Autonomous systems require full observability. Every agent in the AIDLC loop sends telemetry to the Evaluation system, recording the decisions taken, data points considered, tokens consumed, HIL interventions triggered, and cycle times. This data powers the pipeline gates and provides a complete audit trail when something goes wrong.
Telemetry per agent run
Stored in Couchbase, queryable via N1QL
End goal
↓
Cycle time
spec to deployed product
↓
HIL interventions
per feature, over time
→ 0
Human review load
comments per PR at maturity
∞
Development velocity
as the portfolio scales
Get started
Sign in to start connecting repositories, uploading design documents, and building the knowledge graph that every agent in the harness will query.
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