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Forward-deployed engineering

We build it with your team.
Then we keep it running.

Forward-deployed engineers work inside your repositories, standups and release process. We build the agent platform or AI product, measure it with the Harness Method, and stay on monthly to run it, or hand your team the harness to run it themselves.

Discuss your projectStart with a Harness Review

How it works

Review. Build. Run.

Start with a fixed-scope review, sprint or design, or go straight to a build. After launch, Harness Watch keeps it current.

  1. 01 · Review

    Harness Review

    Two to three weeks · fixed price

    An AI agent audit across every layer: where the time and money go, a scorecard on five measures and a prioritized fix plan.

  2. 02 · Build

    Forward-deployed build

    Initial three-month engagement

    Forward-deployed engineers embed with your team and build the agent platform, AI product or the systems behind it, measured with the Harness Method from the first week. Scope, milestones and acceptance criteria are agreed before work starts.

  3. 03 · Run

    Forward-deployed operations

    Monthly retainer, with Harness Watch

    After launch, we stay on to run Harness Watch: a verdict on each relevant model release, a monthly scorecard, incident response and ongoing improvements.

The forward-deployed model

Inside your team, not across the table.

Embedded

Works in your rhythm.

Your repositories, your tickets, your standups. Decisions are made where your team already makes them, with nothing lost in a handoff between sales, product and delivery.

Vendor-neutral

No model to sell.

We test your work across providers and recommend what your evidence supports, including the platform you already pay for.

AI-native

Measured from day one.

A scorecard on quality, speed, cost, control and drift. Agents implement bounded tasks; engineers own the architecture, the review and the release.

The method
Accountable

Finished when your team can run it.

Done means your team ships the next model change through the gate. Harness Watch keeps it current on a monthly retainer.

A good fit

Built for teams that need it shipped and kept running.

  • You have an agent or AI product to ship, and the roadmap can’t wait for a hiring cycle.
  • Your engineers use coding agents, and you want that work reviewable, mergeable and maintainable.
  • The system touches customer data, money or clinical workflows, so permissions, evaluations and audit trails matter.
  • You want it run and improved after launch, not just handed over.

What you keep

You keep everything when we’re done.

  • The working software, in your repositories
  • The scorecard and the cases behind it
  • The regression gate in your pipeline
  • Runbooks and the harness your engineers use

Forward-deployed operations

A monthly retainer, with Harness Watch.

Models, tools and coding agents change every month, and a live agent has to keep up. Harness Watch gives every relevant release a verdict, keeps the evaluations and operating knowledge current, and keeps shipping improvements.

How Harness Watch works
Release verdicts
Each relevant model release runs through your cases: adopt, hold or switch, with the evidence.
Evaluations
Scheduled evaluation runs and regression checks when prompts, tools and settings change.
Harness upkeep
Repository context, permissions and verification gates kept current as models and coding agents change.
Operations
Cost and latency tracking, incident response and recovery for the systems in scope.
Improvements
New capabilities, integrations and fixes as the product evolves.
Autonomy review
Each quarter, whether every workflow's authority expands, holds or contracts.
Readout
A readout for leadership: what shipped, what the scorecard shows and what comes next.

AI-Native SaaS / selected work

Agent Platform Engineering for Enterprise AI

The problem
Give AI agents a way to find, create and update a content platform's structured information.
What we built
Backend AI with Anthropic, OpenAI and Google models, 50+ MCP tools, content APIs and semantic search.
What it made possible
Agents that find information, act on it and power interactive experiences inside compatible AI clients, on a platform used by large enterprises and a unicorn startup.
50+
MCP tools built

Backend AI, API and infrastructure engineering, from architecture through launch.

Read case study

Agent platform engineering

  1. Backend AIAnthropic, OpenAI and Google
  2. 50+ MCP tools builtDiscover, create and update content
  3. APIs and searchStructured content and semantic retrieval
  4. Platform infrastructureSupports agents and MCP Apps
The platform's agent capabilities, layer by layer.

Who we work with

Forward-deployed engineers for your kind of team.

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What are you building—or deciding?

Tell us what you’re building, what is getting in the way, and the outcome you need.

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