AI coding agents that speed up your team without lowering quality: specs before code, quality gates before merge. For engineering leaders.
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The Hackett Group:
12agents, 25 skills and quality-gate hooks (Claude Code, OpenCode)
~50delivered tasks per week (406 tasks) with Spec-Driven Development
Zorbit (own product):
Orchestrates coding agents, from worktree to pull request
39ADRs, 45 specs and 14 RFCs: every decision written before the code
The Hackett Group: 12 agents, 25 skills and quality-gate hooks (Claude Code, OpenCode)
~50 delivered tasks per week (406 tasks) with Spec-Driven Development
Zorbit (own product): orchestrates coding agents, from worktree to pull request
39 ADRs, 45 specs and 14 RFCs: every decision written before the code
Everyone uses a coding agent their own way, and the mistake fixed yesterday is back today, because rules and context never reach the repository.
Pull request volume went up, but review became the bottleneck and production bugs did not go down.
Security and legal ask which code and which data leave for AI providers, and nobody has the answer.
Leadership expects a productivity gain from AI, and engineering has no metric to show what changed.
A reading of the repositories, the pipeline and how the team uses agents today, with a baseline for tests and cycle time and the points where agents help or hurt.
The agent implements what was decided: spec, ADR and acceptance-criteria templates, with traceability between requirement, task and code.
The execution loop, agents per role, permissions, rules in AGENTS.md and CLAUDE.md and hooks that run the checks, versioned so that a mistake fixed once stays fixed.
The agent gets only what the task needs: short- and long-term memory, compaction of long sessions, a token budget and handoffs between agents and sessions.
Skills for the team's recurring tasks, loaded on demand (progressive disclosure), with clear triggers, tests and versioning, like any other code.
MCP servers give the agent controlled access to the repository, tickets and docs; subagents with isolated context research, test and review parts of an approved plan.
Agent-written code only goes in tested: a unit, integration and E2E suite expanded with agents, coverage targets and a blocked merge whenever the gate fails.
Automated review before human review, CodeQL, Trivy and gitleaks in the pipeline, automated releases and traces of every agent run, with tokens, cost and time.
A ready answer for security and legal: rules for data, permissions and approved tools, hands-on sessions on the team's own code and metrics to track adoption.
See also: AI agent development, legacy modernization, and software architecture consulting.
Spec-Driven Development operating model with AI coding agents (Claude Code, OpenCode) on the DIAL and Mirador platforms.
Upgrade of critical applications from PHP 5.6 to 8.4, with Claude Code-assisted refactoring.
Tests and gates on a legacy PHP (~160K LOC) and React/TypeScript (~88K LOC) platform shared by ~18 engineers.
Delivery automation on Bankrate's mortgage rate-table marketplace: CI/CD pipelines and infrastructure as code on AWS.
Facing a similar challenge? Tell us the problem and the timeline.
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AI software automation is the use of AI coding agents, such as Claude Code, Codex, Cursor, Copilot and OpenCode, to implement, test and review software from approved specifications, inside a harness of rules, permissions and quality gates in CI/CD. Hire it when the team already uses AI assistants ad hoc and needs a governed, measured workflow.
We read the repositories, the pipeline and the current use of agents and measure the baseline: coverage, cycle time, rework. You receive an assessment with the risks, the rollout plan and the estimate.
We define the AI usage policy, the harness rules (AGENTS.md, CLAUDE.md, permissions, hooks), the spec and ADR templates and the quality gates. You approve what the agent does on its own and what requires human review.
One squad works on the real backlog with the harness, skills and MCP servers configured in the repository. You receive the versioned configuration, the traces and metrics against the baseline and a recommendation to expand, adjust or stop.
We take the standard to the other teams and repositories: gates in CI/CD, tests, automated code review and hands-on training. You receive the workflow in operation and the delivery and cost metrics dashboard.
We document the operating model and prepare the people on your team who will maintain it. The evolution of the harness, skills and gates can continue under a monthly retainer.
| Scope | Typical timeline | Engagement model |
|---|---|---|
| Engineering discoveryRepositories, pipeline, baseline, risks and rollout plan | 1–2 weeks | Fixed-scope project |
| Proof of concept with one squadHarness, skills, MCP servers and hooks in the repository, measured against the baseline | 2–4 weeks | Fixed-scope project |
| RolloutStandards, quality gates in CI/CD, tests, automated code review and team training | 6–12 weeks | Fixed scope or time & materials with a cap |
| Ongoing evolutionMaintenance of the harness, skills and gates by a part-time (fractional) architect | Ongoing | Monthly fractional retainer |
Timelines are typical ranges. Discovery defines the scope and the estimate of each project; the contract can be in USD or BRL.
Want to know which format fits your project?
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In this service AI writes code, which is why the harness comes first: the agent implements what was specified, the gates check the result and a person approves every merge.
Where AI comes inAI coding agents (Claude Code, Codex, Cursor, Copilot, OpenCode) plan, implement, write tests and do the first review from approved specs.
What humans reviewArchitecture, ADRs, acceptance criteria and every merge; the agent does not approve its own work.
Quality gatesLocal hooks and CI block the merge without green tests, minimum coverage and CodeQL static analysis.
Context engineeringAt each step only what the task needs enters the context window, with compaction, prompt caching and a token budget per team and per task.
Traces and metricsEvery agent run produces traces (OpenTelemetry, LangSmith or Langfuse) with tokens, cost, time and the result of the gates.
Permissions and dataThe agent runs with least privilege, no production secrets, no direct deploys and an allowlist of commands; client data never trains models, and we work under NDA.
| Criterion | Ad-hoc AI assistant use | Governed agentic workflow |
|---|---|---|
| Starting point | Each person's free-form prompt, straight in the editor | Spec and acceptance criteria approved before the code |
| Quality | Depends on who reviews and on the time available | Tests, coverage and static analysis block the merge automatically |
| Security and data | Each person decides what goes to the AI provider | One policy: approved tools, least privilege and secret scanning |
| Rules and context | Every session starts from scratch, and what worked stays in the chat | Harness and context engineering: AGENTS.md, skills, specs and ADRs versioned in the repository |
| Measurement | Individual perception of the gain | Traces per run: delivered tasks, cycle time, coverage and token cost |
In four steps: discovery (1–2 weeks), rules before tools, a proof of concept with one squad (2–4 weeks) and the rollout (6–12 weeks). The rules make up the harness: what the agent may read and run, what requires human review and which gates block the merge. The proof of concept runs on the real backlog and is measured against the baseline. The timeline depends mostly on test coverage, CI and the number of repositories and teams.
It is the work around the model that makes an AI coding agent reliable: tools, permissions, rules in AGENTS.md and CLAUDE.md, and checks. It matters because the model is the same for everyone; the harness decides whether a mistake fixed once comes back on the next task. The term, and how it differs from loop engineering, is in our guide “What Is Harness Engineering for AI Agents?”.
It depends on your security policies, the approved model providers and where the team works (terminal, IDE or pull request), which is why the choice comes out of discovery, not out of a ranking. We work with all five and keep the harness in the repository (AGENTS.md, CLAUDE.md, skills and MCP servers), not on each person's machine. When the team needs its own agent, for example for issue triage or review, Deep Agents (LangChain) is one of the options.
They stay under control when there is a policy: which tools and plans are approved, which repositories, files and MCP servers the agent can reach and what never leaves the environment, such as secrets and customer data. We configure these rules in the repository and the pipeline, with secret scanning (gitleaks). The terms of each provider on data retention and use are reviewed with your security team during discovery. We work under NDA.
It depends on the code, the tests and the method, which is why we promise no percentage. What we do is measure: a baseline first, a proof of concept next, tracking delivered tasks, cycle time, coverage, defects and the traces of every agent run. As a reference for a governed workflow: on the program we work on for The Hackett Group, about 50 tasks were delivered per week, 406 in total, with 95% coverage gates.
We recommend that your company contracts the tools and pays for model usage on its own accounts, so that licenses, configuration and history stay with you. The final arrangement is set in the proposal. In discovery we estimate consumption per person and per type of task; in the rollout, token cost is tracked per team, with defined limits, and context engineering and prompt caching avoid paying twice for the same context.
Yes, on one condition: what the AI produces goes through the same gates as code written by people. Agents write unit, integration and E2E tests from specs and do the first review of every pull request; human review decides the merge. On The Hackett Group program, this model sustains about 4,000 tests with 95% coverage gates and about 380 Playwright E2E cases.
They do, as long as the test safety net comes first. On legacy code, the agent starts by reading and documenting the code and writing tests that pin down current behavior; only then does it change anything. At Regal Rexnord, we used Claude Code to reverse-engineer undocumented legacy code during the PHP 5.6 to 8.4 upgrade. A full modernization program is covered by the legacy modernization service.
AI-focused IT consultancy
Senior engineering from discovery to delivery: whoever designs the architecture also writes and reviews the code.
Master Timm · Updated on
In the first conversation we look at how the team uses AI today, the tests and the pipeline, and tell you where to start: a proof of concept with one squad or the test foundation.
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