We support your team and assess your engineering environment across the dimensions that determine how AI agents behave. Then we work with your team to structure it so agents follow your rules, not break them. Prioritized roadmap in 2 to 3 weeks.
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72 assessment criteria
5 maturity levels
2-3 weeks to first roadmap
Three months after deploying AI coding agents, an engineering team found their agents were consistently generating code that violated service boundary rules. Not because the AI was bad, but because nobody had told it the rules existed. That's a configuration problem, not a model problem.
If any of these sound familiar, your AI environment needs structure:
Harness Engineering is the discipline of structuring the constraints, tools, documentation, and feedback loops that allow AI coding agents to operate productively and in alignment with your standards. It's not a platform you install. It's a consulting engagement where our engineers work alongside your team.
The companies that have solved this (Stripe, Shopify, Block) built these patterns internally. Our consultants package those patterns into a repeatable, measurable framework for your organization. Tool-agnostic: works with Cursor, Copilot, Claude Code, Codex, or whatever comes next.
Each dimension is scored on 5 maturity levels, with specific criteria our consultants evaluate through interviews, codebase review, and workflow observation.
Can your AI agents see and respect your service boundaries? We assess whether your codebase has the structure and rules that prevent agents from violating design patterns.
Do your agents get fast feedback when they make mistakes? We evaluate how your CI/CD and automated checks correct agent errors before they reach code review.
Can your agents find the context they need to make good decisions? We review whether your documentation gives agents the same understanding a senior team member has.
Do your agents know what to build before they start writing code? We assess how work is broken down, scoped, and assigned to agents with clear objectives.
Who is accountable for AI-generated code? We evaluate your review process, from automated quality checks to human sign-off on architectural decisions.
Can you run multiple agents across teams without chaos? We assess your readiness to scale from individual use to coordinated, organization-wide agent workflows.
Is your team learning how to work with agents, or just using them? We evaluate adoption patterns, skill development, and whether teams share what works.
The framework behind our assessment didn't come from a whiteboard session. It has three layers of validation.
Most AI transformation frameworks are built top-down: a consultancy decides what "good" looks like and sells it.
We went bottom-up. We studied what engineering teams actually do when they successfully integrate AI agents into their workflow. Then we structured those patterns into a repeatable assessment.
The result is a framework grounded in what works, not what sells.
Engineering blogs, open-source repos, published research from teams running AI agents at scale. Systematically analyzed, not cherry-picked.
Every practice has been tested with real engineering teams. Not theoretical checklists, but patterns validated through implementation.
Structured into a scoring framework with 5 maturity levels. Repeatable, measurable, comparable across organizations.
Four consulting modules, one clear path. Each engagement is scoped, time-bound, and designed to leave your team more capable than when we started.
Our consultants interview your tech leads and developers, review your codebase structure and workflows, and deliver a maturity score across all 72 criteria with a prioritized roadmap and executive summary.
Clear picture of where you stand, with immediate actionable improvements
We work with your architects to set up agent instruction files across priority repositories, implement guardrails agents respect, connect agents to your organizational context, and install quality gates that catch issues before review.
Consistent, reviewable AI contributions across pilot teams
Together we integrate agent workflows into your CI/CD pipeline, set up impact metrics so you can measure ROI, coordinate multi-agent workflows across repositories, and roll out what works to all teams. Internal team members are trained to own the framework going forward.
Measurable productivity gains, AI as a multiplier not an experiment
Your team owns the framework. We provide quarterly check-ins to re-score and track progress, update recommendations as AI tools evolve, benchmark your progress against industry peers, and offer on-demand advisory for new challenges.
Full internal ownership with ongoing expert support
Each engagement is scoped, time-bound, and designed to leave your team more capable than when we started.
They probably can, with significant trial and error. The question is whether you want to spend 6-12 months discovering the patterns yourself, or bring in consultants who have already synthesized them from 60+ sources. We compress that discovery into 2-3 weeks and hand the framework to your team to own.
Your platform team owns infrastructure reliability. This is a different layer: the documentation, constraints, context, and feedback loops that determine whether an agent makes good decisions or confidently wrong ones. Our consultants work with your platform team, not instead of them.
You can, but you'll assess against your own mental model of what good looks like, which is shaped by your existing practices. The value of an external assessment is a framework derived from 60+ sources showing what mature AI engineering looks like outside your context, with specific sequencing of what to fix first and what to ignore.
Every assessment includes a re-scoring checkpoint: we measure improvement against the initial baseline. Our framework is built on 60+ primary sources from engineering teams running agents at scale, and every practice has been applied in production environments. We're also building a cross-client benchmark dataset that will allow organizations to compare their progress against industry peers. Early results are available under NDA.
The assessment is structured around interviews, workflow observation, and codebase architecture review, not bulk code extraction. We define the access scope together before the engagement starts, with NDA and data handling agreements in place. Your security and legal teams will have full visibility into what we access and how.
The framework is tool-agnostic by design. Architectural guardrails, documentation standards, feedback loops, and quality gates remain valuable regardless of which AI model or tool your team uses. Our Ongoing Advisory module exists specifically to update the framework as tools change.
AI coding agent adoption is accelerating, but organizational readiness isn't keeping pace. Every quarter of unstructured adoption means more bad patterns to undo and more inconsistency to clean up later.
Stripe, Shopify, and Block have solved this internally. They document the patterns but don't offer them as a service. Our consultants bridge that gap: we bring those patterns to your team in a structured, hands-on engagement.
The Assessment is a 2-3 week consulting engagement. Our team interviews your engineers, reviews your codebase structure, and delivers a maturity score with a prioritized roadmap. Requires about 4-6 hours of your team's time across 3 sessions.
Harness Engineering by FairMind Consulting. Assessment results in 2-3 weeks.