A Demo Agent Takes a Weekend. A Production Agent Takes a Harness.
Evals, tools, memory, permissions, observability. The unglamorous half is the half that decides whether the agent survives real use.
What is an AI agent harness?
An agent harness is everything around the model that makes an agent dependable: evals, tools, memory, permissions, and observability. Jamak designs and builds production agents with that harness included. This is agent engineering, not a consulting engagement. Month-to-month.
How it works
Define the task
- The exact task, its inputs, and what done looks like
- Which decisions the agent takes, and which it escalates
- Whether it should be an agent at all — a script is sometimes the honest answer
Build agent and harness
- Tools the agent may call, and the ones it may not
- Memory that is scoped, not a dump of everything
- Permissions and observability so a bad run is visible, not silent
Run evals on every change
- An eval suite built from your real cases
- A regression run when a model or prompt changes
- A review of what to widen and what to lock down
What a production agent needs that a demo does not
| Harness component | What it prevents |
|---|---|
| Eval | Silent regressions when a provider ships a new model version |
| Tools | An agent that can talk but cannot finish the job |
| Memory | A context dump that makes every run slower, costlier, and less reliable |
| Permissions | The agent taking an action you would have to explain to a customer |
| Observability | Spending days reconstructing why it did what it did |
Frequently asked questions
How long does a production agent take?
The agent itself is rarely the slow part. Integrations are. A demo is a weekend. A production agent takes as long as the systems it has to touch. We will not name a date we cannot keep.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent completes tasks — it uses tools, takes actions in your systems, and decides when it is done. That is why an agent needs a harness and a chatbot mostly does not.
What stops an agent doing something expensive or wrong?
Permissions and a cost ceiling. We define which actions the agent may take alone, which need a person, and how much a single run is allowed to spend. Anything past those boundaries escalates instead of proceeding.
How do you know an agent is actually working?
An eval suite built from your real cases, run on every prompt and model change. Without one, "it works" only ever means it worked the last time somebody looked.
Do you work in English and Spanish?
Yes. The studio is bilingual. The working conversation, the docs, and the handoff can be English, Spanish, or both.
We need a whole AI program, not one agent.
Then start on the AI implementation page. That covers adoption across the business — finding the first workflow, integration, and handover. This page is the agent engineering inside it.
Talk to us
Request a quote. We will read the task and say whether it should be an agent, a script, or left alone.