Production AI agent development
The short answer
Production AI agent development is the engineering that turns a working demo into a system you can operate: evaluation suites, guardrails, tool integration, observability, rollback and an owner. Branemind builds agents to that standard, and will tell you when a workflow does not need an agent at all.
The demo worked. That was never the hard part
A prototype that answers well in a meeting is a few days of work. What stops it reaching production is everything around it: nobody can say whether a change made it better, there is no record of why it did what it did, it has no defined behaviour when a tool fails, and no one owns it at 2am.
What it costs today
- Pilots that stall indefinitely because no one can approve the risk
- Changes shipped on vibes, since there is no eval to regress against
- Incidents with no trace, so the fix is a prompt guess
- Cost that is discovered on the invoice rather than per request
What we build, and where we stop
The second list matters as much as the first. A boundary that is agreed in writing before the build is the difference between a system your compliance team signs off and one they discover.
What we build
- An eval suite built from your real cases, with a pass bar agreed before build
- Guardrails at the input, the tool call and the output
- Tool and API integration with typed contracts and failure behaviour
- Tracing across retrieval, tool calls and generations, with per-request cost
- Versioning of prompts, models and tools, with a rollback that has been tested
- A runbook and a named owner, because an unowned agent is an outage waiting
What we do not do
- Build an agent where a rules engine or a form would do the job better
- Ship without an eval baseline, however short the timeline
- Hide the model provider choice. You should be able to change it
- Leave you dependent on us to make a change
How it works
- 01
Define the loop, and the failure
We start from the business loop and what a wrong answer costs. That number sets how much control the design needs, and sometimes it ends the project early, which is a good outcome.
- 02
Baseline before building
Current volume, handling time, error rate and cost are captured first. Without a baseline there is no honest way to report an outcome later.
- 03
Build the eval before the agent
Real cases with known-good outcomes, including the awkward ones. The eval is what makes every later change a measurement rather than an opinion.
- 04
Integrate tools with contracts
Each tool has a typed interface, a timeout, a retry policy and a defined behaviour when it fails. Most agent incidents are tool incidents.
- 05
Instrument, then ship narrow
Tracing and cost accounting go in before the first real user. The first release is deliberately narrow, with a rollback that has been exercised.
- 06
Hand over
Runbook, eval suite, dashboards and the ability to change prompts and thresholds without us.
What it connects to
- Your existing SaaS and internal APIs
- Model providers, with provider choice kept swappable
- Vector and search infrastructure
- Identity and role-based access
- Observability and logging stacks
- Cloud accounts, including deployment into your own VPC
What you need in place
We would rather tell you this before a proposal than during one.
- A named business owner for the loop, not only a technical sponsor
- Access to real examples, including the failures
- Agreement on what the agent is not allowed to do
- A decision on where data may be processed, made before build
Controls, approvals and what happens when it fails
Evaluation as a gate
Changes run against the eval suite before release. A regression blocks the release rather than being noticed by a customer.
Layered guardrails
Input validation, tool call authorisation and output checks. A single prompt instruction is not a control.
Full trace
Retrieval, tool calls, generations, latency and cost on one timeline per request, so an incident is diagnosable.
Tested rollback
Prompt, model and tool versions are pinned and revertible, and the revert is exercised before go-live rather than during an incident.
The proof
What to instrument before your first agent goes live
Traces, evals and cost caps are not a phase two. Here is the minimum you need in place on the day you put an agent in front of a customer.
- Issues caught pre-customer
- 83%
- Mean time to diagnose
- < 10 min
- Cost variance
- ±6%
What we measure, and what we do not promise
- What we measure
- Eval pass rate on your own cases, escalation and containment rates, per-request cost and latency, incident count and mean time to diagnose.
- What good looks like
- A change can be shipped on a Tuesday afternoon by someone who is not the original author, because the eval says whether it is safe.
- When we say no
- If the loop is deterministic, low variance and well served by a rules engine, an agent adds cost and failure modes for no gain. We would rather say that in the first call than in month three.
What this ships on
Questions we get asked
What makes an AI agent production-ready?
Five things that a demo does not have: an evaluation suite built from real cases so changes can be measured, guardrails at input, tool call and output, tracing that makes an incident diagnosable, a tested rollback for prompts, models and tools, and a named owner with a runbook. Accuracy in a demo is necessary and nowhere near sufficient.
How should we evaluate an agent before production?
Build a set of real cases with known-good outcomes, weighted towards the awkward ones, and agree the pass bar before development starts. Then measure every change against it. Evaluating after the build, against cases chosen once the behaviour is known, mostly measures your own optimism.
AI agent, chatbot or workflow automation, which do we need?
If the path is fixed and the inputs are structured, workflow automation is cheaper and more reliable. If the task is answering questions from known content, a retrieval assistant is enough. An agent earns its complexity only when the work needs judgement across several systems and the path varies by case.
Can you deploy into our own cloud?
Yes. The runtime can run in your VPC, and provider choice can be constrained for data residency. Decide this before the build, because retrofitting a residency constraint is expensive.
Review your path to production
A readiness read on evals, controls and ownership.