Capacity when the deal lands
Add a forward-deployed team for the agent work without hiring ahead of the pipeline.
For agencies, consultancies and platform vendors
The agent engineering partner behind your client work.
When your clients need agents, MCP integrations or evaluation work your bench doesn't cover, we forward-deploy under your engagement and deliver to your standard.
What we solve
The agent work your clients are asking for.
Depth your bench lacks
Agent runtimes, MCP tools, retrieval, evaluations and the platform work underneath them.
Your client, your relationship
We work inside your engagement, your delivery process and your communication plan.
Proof
Work we have already done.
We built the agent platform behind a product used by large enterprises and a unicorn startup: 50+ MCP tools, semantic search and model integrations with Anthropic, OpenAI and Google.
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 studyAgent platform engineering
- Backend AIAnthropic, OpenAI and Google
- 50+ MCP tools builtDiscover, create and update content
- APIs and searchStructured content and semantic retrieval
- Platform infrastructureSupports agents and MCP Apps
How we engage
Forward-deployed engineers, on terms you can plan around.
Scoped builds on agreed milestones, Harness Watch for ongoing client work, or a Harness Review to help you scope and win the deal.
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.
You get: Working software, a scorecard on quality, speed, cost, control and drift, the harness your engineers keep using, release gates, runbooks, and a handoff your team runs with.
Explore this optionForward-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.
You get: Release verdicts, a monthly scorecard, a quarterly autonomy review, incident response, improvements and a leadership readout.
How Harness Watch worksHarness 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.
You get: Harness Map, Scorecard, model and settings comparison, control findings, fix plan, and the test runner in your repository.
Explore this optionOr start where your agents are today
Build
Building something new
Add an AI feature, connect it to your systems, or build an agent workflow, measured from the first week.
Forward-deployed build · initial three-month engagementEvaluate
Agents already live
Find where the time and money go, score the agent on five measures and fix what matters first.
Harness Review · two to three weeksRun
Running and improving agents
Monitor agent behavior, respond to incidents, improve the system and evaluate model changes against your own cases.
Forward-deployed operations with Harness Watch · monthlyDesign
Not sure an agent fits yet
Choose the workflow worth automating, compare the ways to do it and prove a first version.
Harness Design · three weeksGo deeper
Read how we approach it.
Start a conversation
What are you building—or deciding?
Tell us what you’re building, what is getting in the way, and the outcome you need.