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Your engineers use AI agents. Those agents don't know your architecture, your boundaries, or your standards.

Our consultants assess your environment across 72 criteria and 7 dimensions, then work with your team to structure it so AI agents behave like a senior engineer who knows the rules. Prioritized roadmap delivered in 2-3 weeks.

See How It Works
Framework built on 60+ documented engineering practices from teams running AI agents in production

7

Assessment Dimensions

72

Assessment Criteria

5

Maturity Levels

2-3

Weeks to First Roadmap

Every organization invests in AI coding tools. None invest in the approach that makes them productive.

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:

AI-generated code violates your architectural patterns, and reviewers catch it too late

Review cycles are getting longer, not shorter, since adopting AI tools

Senior engineers say "the AI slows me down more than it helps"

You can't report AI ROI to the board because there is no measurement system

Every developer uses AI differently, no shared practices, no shared standards

You're locked into one vendor with no multi-agent strategy

The model isn't the bottleneck. The environment is.

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.

We assess your organization across 7 dimensions

Each dimension is scored on 5 maturity levels, with specific criteria our consultants evaluate through interviews, codebase review, and workflow observation.

01Architecture & GuardrailsCan 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.
02Tooling & Feedback LoopsDo 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.
03Documentation & KnowledgeCan 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.
04Planning & DirectionDo 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.
05Quality & ReviewWho is accountable for AI-generated code? We evaluate your review process, from automated quality checks to human sign-off on architectural decisions.
06Orchestration & ScaleCan you run multiple agents across teams without chaos? We assess your readiness to scale from individual use to coordinated, organization-wide agent workflows.
07Culture & AdoptionIs your team learning how to work with agents, or just using them? We evaluate adoption patterns, skill development, and whether teams share what works.

Most organizations score between Level 1 and Level 2. The assessment tells you exactly where you stand.

Built from evidence, tested in production

The framework behind our assessment didn't come from a whiteboard session. It has three layers of validation.

Why this matters

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.

60+ primary sources

Engineering blogs, open-source repos, published research from teams running AI agents at scale. Systematically analyzed, not cherry-picked.

Applied in production

Every practice has been tested with real engineering teams. Not theoretical checklists, but patterns validated through implementation.

72 practices, 7 dimensions

Structured into a scoring framework with 5 maturity levels. Repeatable, measurable, comparable across organizations.

Case studies from early adopters coming Q2 2026.

Early results available under NDA

How we work with your team

Four consulting modules, one clear path. Each engagement is scoped, time-bound, and designed to leave your team more capable than when we started.

1
Weeks 1-3

Assessment

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.

  • ✓Maturity Score
  • ✓Prioritized Roadmap
  • ✓Executive Summary
  • ✓Quick Wins Playbook
→Clear picture of where you stand, with immediate actionable improvements
2
Weeks 4-10

Foundation

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.

  • ✓AGENTS.md per repo
  • ✓Feedback Loops
  • ✓Quality Gates
  • ✓Pilot Team Training
→Consistent, reviewable AI contributions across pilot teams
3
Months 3-6

Scale

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.

  • ✓CI/CD Integration
  • ✓Impact Metrics
  • ✓Multi-agent Orchestration
  • ✓Org-wide Rollout
→Measurable productivity gains, AI as a multiplier not an experiment
4
Quarterly, from month 7

Ongoing Advisory

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.

  • ✓Quarterly Re-score
  • ✓Benchmark Report
  • ✓Practice Updates
  • ✓On-demand Advisory
→Full internal ownership with ongoing expert support

What's included in each module

Each engagement is scoped, time-bound, and designed to leave your team more capable than when we started.

Start here

Assessment

2-3 Weeks

  • ✓Our consultants interview your tech leads and developers
  • ✓We review your codebase structure and workflows
  • ✓You receive a maturity score across 72 criteria
  • ✓We deliver a prioritized roadmap with clear next steps
  • ✓Executive summary ready for your leadership team

Foundation

6-8 Weeks

  • ✓We set up agent instruction files across your repositories
  • ✓We configure feedback loops and quality gates with your team
  • ✓Hands-on training for 2-3 pilot teams
  • ✓Documented playbook your team owns going forward

Scale

3-4 Months

  • ✓We integrate agent workflows into your CI/CD pipeline
  • ✓We help coordinate multi-agent use across repositories
  • ✓Organization-wide rollout with your team leading
  • ✓Internal team members trained to maintain the framework

Ongoing Advisory

Quarterly

  • ✓Quarterly check-in to re-score and track progress
  • ✓Updated recommendations as AI tools evolve
  • ✓On-demand advisory for new challenges
  • ✓Benchmark your progress against industry peers

Questions Engineering Leaders Ask

Why can't my team figure this out themselves?+
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.
Isn't this what our platform engineering team should do?+
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.
We can do a self-assessment.+
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.
How do you validate your results?+
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.
What about security? You're asking for deep access to our codebase.+
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.
What happens when AI tools change? Is this framework obsolete in 12 months?+
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.

The window is now

The gap is widening

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.

The patterns exist, they're just not packaged

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.

Start with a conversation. We'll tell you if we can help.

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.

No commitment beyond the assessment. If the data doesn't make the case, we'll tell you.