The 10-Agent Pipeline
How we build software
with AI.
Not AI-assisted development. AI-orchestrated development, with humans as architects and reviewers. Ten specialized agents. Each with a single job. All coordinated by our engineers.
Free consultation · No commitment · Honest scope assessment
Agent sequence
Ten agents. One pipeline.
Each agent has a specific input, a specific output, and a specific failure mode. No agent starts until the previous one has produced a verified output. Humans review at critical junctions — not as a formality, but as an architectural requirement.
Spec Agent
Brief → Executable specification
Receives your unstructured brief, asks targeted clarifying questions, and produces a structured specification document with acceptance criteria. The Spec Agent treats ambiguity as a blocking error — it does not proceed until requirements are unambiguous.
Input
Unstructured brief: a paragraph, a voice note transcript, a Notion doc, a Figma link
Output
Structured specification: feature list, acceptance criteria, out-of-scope definition, open questions resolved
Includes back-and-forth Q&A with the client or product owner
Architecture Agent
Specification → System design
Designs the system: component breakdown, data models, API contracts, infrastructure topology, and third-party service selection. Flags architectural risks and trade-offs for human architect review before implementation begins.
Input
Structured specification from Spec Agent
Output
Architecture document: component diagram, data model, API contracts, infrastructure choices, flagged risks
Human architect reviews output before proceeding — this is not optional
Scaffold Agent
Architecture → Project skeleton
Generates the project skeleton from the architecture document: folder structure, dependency manifests, environment configurations, CI/CD pipeline templates, and base boilerplate. Creates a runnable (empty) project that the Implementation Agent can populate.
Input
Architecture document
Output
Initialized repository: folder structure, package.json / requirements.txt, .env templates, CI/CD config, README skeleton
Deterministic and fast — the scaffold is generated, not designed
Implementation Agent
Spec + scaffold → Working code
Writes feature code against the specification. Works file by file, following the architecture document, committing frequently with descriptive commit messages. Consults the spec on every decision — the spec is the source of truth, not the agent's priors.
Input
Spec document + project scaffold + architecture document
Output
Implemented features with passing basic smoke tests, committed to feature branches with descriptive history
Scope-dependent. A single feature takes 2-3 hours; a full MVP may span multiple sessions
Review Agent
Code → Written review
Reads every line of the implementation. Checks for security vulnerabilities, code quality issues, spec compliance, missing edge case handling, and architectural drift from the design document. Produces a written review with prioritized findings.
Input
All implementation code + original spec + architecture document
Output
Review report: security findings (P0/P1/P2), spec gaps, quality issues, recommended changes — prioritized by severity
Catches ~85% of security issues and ~70% of spec gaps before QA
Test Agent
Spec + code → Test suite
Generates unit tests, integration tests, and edge case tests derived directly from the spec's acceptance criteria. Every acceptance criterion becomes at least one test. Edge cases that appeared in the Review Agent's output get targeted tests.
Input
Spec acceptance criteria + implementation code + Review Agent findings
Output
Test suite: unit tests, integration tests, edge case tests — all passing before proceeding
Average 94% test coverage on new code. Higher for business logic, lower for UI rendering
Debug Agent
Test failures → Patches
Runs the full test suite, investigates failures, traces root causes, and patches bugs. Iterates until the test pass rate meets the threshold defined in the architecture document. Logs every bug found with root cause analysis for the project record.
Input
Test suite + implementation code
Output
Patched code with passing test suite + bug log with root cause analysis for each resolved issue
Depends on bug count and complexity. More than 4 hours signals a spec or architecture issue — we escalate
Documentation Agent
Code + spec → Documentation
Writes inline code documentation, README, API documentation, deployment guide, and operations runbook. Documentation is generated from the actual code, not from memory — it is always synchronized with the implementation.
Input
Final implementation code + spec document
Output
Inline docs, README, API reference, deployment guide, operations runbook — all versioned with the code
Documentation is a deliverable, not an afterthought. Clients get it as part of every project
Deploy Agent
Code → Running production system
Configures infrastructure based on the architecture document, sets up CI/CD pipelines, manages environment variables and secrets, configures health checks, and executes the first production deployment. Does not proceed without a passing test suite.
Input
Final code + infrastructure spec from architecture document + credentials
Output
Running production environment: cloud infrastructure, CI/CD pipeline, configured secrets, health checks active, deployment verified
Infrastructure-as-code means this is reproducible — staging and production environments are configured identically
Monitor Agent
Live system → Observability
Configures the observability stack post-launch: error tracking, performance monitoring, uptime checks, and alerting rules. Sets baseline metrics during the first 48 hours and configures alerts against those baselines. Runs continuously.
Input
Running production system + performance requirements from spec
Output
Observability stack: error tracking, APM, uptime monitoring, alerting rules, baseline metrics, on-call runbook
Initial setup takes 1-2 hours. Monitoring runs continuously and feeds back into the Services team
Human roles
AI writes. Humans architect.
The pipeline does not replace engineers. It changes what they spend their time on. Human engineers handle the 20% of decisions that require genuine judgment — the work that cannot be specified upfront.
Architect
Between agents 02 and 03
- Reviews Architecture Agent output against real-world constraints not captured in the spec
- Approves or modifies the system design before implementation begins
- Considers: team skill set, existing infrastructure, compliance requirements, budget constraints
- Acts as the final technical authority — no implementation without architect sign-off
Review Engineer
Between agents 05 and 06, and post-deploy
- Reviews Review Agent findings and validates severity assessments
- Makes judgment calls on architectural trade-offs the agent flagged but could not resolve
- Handles the 15% of security issues and 30% of spec gaps the Review Agent misses
- Approves the release before the Deploy Agent proceeds
Operations Engineer
Post-deploy, ongoing
- Interprets Monitor Agent alerts and decides whether to page, investigate, or ignore
- Handles failure scenarios that require judgment: data migrations, rollbacks, incident response
- Feeds operational learnings back into spec and architecture templates for future projects
- Maintains the relationship with the client — the human face of delivery
“AI writes. Humans architect. You scale.”
The agents handle throughput — volume, repetition, consistency, documentation. The engineers handle judgment — trade-offs, constraints, edge cases, client relationships. Neither works without the other. That is the model.
Pipeline performance
What the numbers say.
Internal data across 40+ projects built on the pipeline since we formalized the approach in mid-2025. These are honest averages — not best-case results.
Ready to ship
Use this pipeline for your project.
Bring us your idea, your broken prototype, or your backlog. We run the pipeline, you get a production-ready product. Our engineers stay on to keep it running.
Free consultation · Honest timeline and cost estimate · No enterprise sales process