Your own team
Best when
You have eval and platform engineers with time for this work.
Alongside it
A Harness Review leaves the measurement system in your repository, so they move faster.
Why TeqEngine
Why teams choose independent forward-deployed engineers.
You can build it yourselves, ask your model vendor, hire a large firm or buy a tool. Here’s what we do differently, and when each option fits best.
What you can hold us to
Every engagement, in writing.
- Scope, milestones and acceptance criteria are agreed before work starts.
- Every finding comes with its evidence, an owner and the next decision.
- A weekly status and decision log, in writing.
- You keep everything when we're done.
- The work is finished when your team can ship the next model change through the gate.
Five reasons
Built, measured and owned.
Each reason links to the work or the method behind it, so you can check it before the first conversation.
We've built it and run it.
We built the backend AI, APIs and agent tooling behind a platform a unicorn startup and Fortune 500 companies use, with 50+ MCP tools across Anthropic, OpenAI and Google models. We also led engineering for a regulated healthcare platform through its acquisition by an a16z-backed company. We review agents the way people who have had to run them do.
Read the agent platform caseThe whole system, not just the evals.
Interface, services, orchestration, tools, models and settings, plus memory and human oversight, measured on quality, speed, cost, control and drift. Most slow or wrong answers come from somewhere a single eval never looks.
See a sample Harness ScorecardNo model to sell.
We don't resell models or platforms. We test your work across providers and recommend what your evidence supports: a different model, the platform you already pay for, simpler automation, or no agent at all.
How a model change is decidedYou keep everything when we're done.
Code, evals, test sets, dashboards and runbooks live in your repository. The work is finished when your team can ship the next model change without us. Keep us on monthly when you want us to run it.
Delivery and ownershipReady for the next model.
Harness Watch reruns your scorecard on each relevant release and tells you whether to adopt, hold or switch, what you can now delete, and when your agents have earned more autonomy.
Harness Watch
How the options compare
The same agent, four other ways to get it done.
Every option has real strengths. This is how they differ on the questions that decide whether an agent keeps working as the models change.
| Your own team | A model vendor's engineers | A large consultancy | A tool or platform alone | TeqEngine | |
|---|---|---|---|---|---|
| Engineers who have built agent platforms do the work | Depends on the hire | Yes | Varies | No engineers | Yes |
| Covers every layer, interface to model | Depends on the team | Mostly their stack | Varies | Measures some layers | Yes |
| Tests your work across model providers | Possible | Their models first | Varies | Some tools | Yes |
| Recommends not building when that's the better call | Your call | Their platform first | Varies | Not their role | Yes |
| You keep the code, evals and tooling | Yes | Varies | Varies | Data lives in the tool | Yes |
| Fixed scope and fixed price to start | Not applicable | Depends on the agreement | Depends on the agreement | Subscription | Yes |
| Keeps measuring as the models change | When staffed | For their models | When retained | Partly | Yes, with Harness Watch |
Choosing the right option
When each one fits best.
Each option is strongest somewhere. Here is where, and how we work alongside it.
Questions buyers ask
Why not the alternative?
Why not just hire?
Keep building your team. AI skills are now the hardest to hire: ManpowerGroup's 2026 Talent Shortage Survey of 39,063 employers puts AI model and application development first. We add that capability now, leave it in your repository, and your team owns it from there.
Why not our model vendor's engineers?
They know their models best, and their business is the adoption of those models. We have no model to sell: we test your work across providers and recommend what the evidence supports. We also review a vendor's build independently.
Why not a large consultancy?
For a global rollout with change management at scale, they're the right lead. For the agent itself, the engineers who scope the work do the work, with a fixed scope and a fixed price to start. We work inside their program when that's the structure.
We already use an eval or observability tool.
Keep it. Tools record and score. We decide what good means for your business, calibrate the checks, find the slow or costly step across every layer, ship the fix, and leave it running on the tool you already pay for.
We already have evals.
Good. We start from them and add the other four measures: speed, cost, control and drift. Then the next model decision has evidence behind it.
Why not wait for better models?
Better models are coming. The question is whether you'll know when one is better for your workflow, and what it costs. A regression gate lets you adopt it quickly and delete what you no longer need.
What do we keep?
Everything: code, evals, test sets, dashboards and runbooks, in your repository.
What happens after the engagement?
Run it yourselves, or keep us on monthly for Harness Watch and ongoing improvements.
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.