AI Services · Equip
Coding agents held to your standards by checks they can't switch off.
We install the rules your coding agents follow and the guards that stop dangerous actions, and your team keeps the method.
Every team's agent setup is drifting apart
Rollouts get measured in seats and prompt libraries. Each team writes its own instructions, nobody knows which ones work, and agents quietly game the tests they’re asked to pass. At teams with high AI adoption, pull requests merged without any review rose 31% (Faros, vendor study, 2026).
More instructions rarely help. Over-constrained agents get worse, not better. What works is a small set of rules the agent can actually follow, and enforcement for the few failures that would be costly.
What you get
Harness Foundations
4 weeks · fixed fee
- A short rulebook that loads at the start of every agent session
- 5 or 6 core standards, written for the agent
- Guards that block dangerous agent actions before they run
- A minimum set of automatic build checks: secret scanning, a block on placeholder tests and required checks
- An automatic check on agent settings, so drift shows up in review
Harness Install
8–12 weeks · fixed fee
- Your own standards repository, co-authored with your engineers using our method
- Skills the agent pulls in by task, agent roles and a delegation playbook
- A conformance audit and a drift sweep across your repositories
- Templates for new repositories, and trained internal champions
Agent tune-up
1–3 weeks · fixed fee
For in-house agents that got worse as guardrails piled up. It covers a clean-up of stale instructions, a four-question test every rule must pass, redesigned tools and a review of real agent runs.
Standards retainer
capped monthly retainer
A quarterly drift audit, upkeep as models change, and tuning of the build checks.
New to Techtura? Start with a pilot
We install the harness on one repository with one team in about 3 weeks, at an introductory fixed fee for new clients, with agreed success metrics and a path to Foundations.
Rules that hold where it counts
Enforcement, not templates
Prompt libraries and instruction templates are suggestions. We install checks the agent can’t edit, so the rules that matter are enforced, not suggested.
Standards tested on agents
We test how well agents follow our standards in blind-scored runs, and cut what doesn’t change behavior. Shorter, sharper rules work as well, at a fraction of the length.
Whichever agent wrote the code
The build checks run in your CI, so they apply to code from Claude Code, GitHub Copilot, Cursor, AWS Kiro or a person alike. Your standards live in your repository, and your champions learn to maintain them without us.
Our own repositories run on this harness
21
standards and about 480 rules written for agents, in daily use across our engineering estate
81
blind-scored test runs: standards about one-eighth the length worked as well as the full versions
411
automated tests on the harness’s hooks and checks, run on every change
Questions engineering leaders ask
Which agents and tools does it support?
Claude Code, GitHub Copilot, Cursor, OpenAI Codex, AWS Kiro and any tool that reads AGENTS.md or Agent Skills. We deliver your standards in each tool’s own format, as skills, file-scoped rules, custom agents and guard hooks, so they load where your engineers already work. The build checks run in CI, so they apply to every pull request, whichever agent or engineer wrote it.
Does it work with managed tools such as AWS Kiro?
Yes. Your standards ship as Kiro steering files that load by file type, as Agent Skills, and as custom agents whose write permissions match their role, alongside your Kiro specs. Guard hooks run in the Kiro IDE and CLI, and the same CI gates check every pull request, including those opened by Kiro’s cloud agent. Where the vendor runs the model for you, we build oversight on what the platform exposes, such as prompt logs in your own AWS account.
How do we know it worked?
With the AI Engineering Baseline, before and after: lead time, change-failure rate, review latency and model cost per merged pull request.
Will this slow our engineers down?
No, it speeds them up. Agents follow your standards from the first draft, so fewer pull requests bounce in review, and the build checks catch committed secrets, placeholder tests and skipped checks at the pull request, not months later as technical debt or a security issue someone has to hunt down. Everything else is guidance the agent pulls in only when the task needs it.
What size of team is this for?
Engineering organizations of roughly 20 to 300 engineers, usually championed by the platform or developer-experience lead.
A free way to start
Take the AI Engineering Scorecard: 15 questions on measurement, build checks, cost caps, evaluations and governance. The gap report shows where your harness is weakest.
Get your agents working to your standards.
Take the free AI Engineering Scorecard, or name the coding agents your teams use and we’ll point you to the right first step.