Services · Agent Factory
Agent Factory: How We Build AI Agents
A repeatable way to design, test, deploy, and govern AI agents for your business, so each agent is built to reach production with clear limits instead of stalling as a one-off experiment.
What is an agent factory?
An agent factory is a repeatable system for producing AI agents. It reuses shared components (model access, tools and integrations, data access, and guardrails), evaluates every agent the same way before release, and deploys, monitors, and governs agents under one set of rules. It is the opposite of a one-off pilot that works in a demo and never reaches production.
Model access
One managed way to call the language models every agent uses, instead of each agent wiring up its own.
Tools and integrations
The connections an agent uses to read from and write to your systems, built once and reused.
Data access
Rules for which of your data an agent may see and which may never enter a prompt.
Guardrails
Limits on what an agent may do, and the points where it must stop and ask a person.
The stations of an AI agent factory
An AI agent factory moves each agent through the same stations: pick the workflow and define success, design its tools and permissions, evaluate it against test cases before release, require human approval for risky actions, deploy it with logging, then monitor and improve it. Post-launch monitoring is scoped per engagement.
Pick the workflow and define success
Choose one workflow the agent will run, and write down what a correct result looks like before anything is built.
Design tools and permissions
Give the agent only the tools it needs. Least privilege: list what it may do, and what it may not, in writing.
Evaluate before release
Run the agent against test cases drawn from the real workflow and hold it to an agreed threshold. An agent that misses it does not ship.
Human approval for risky actions
Actions with real consequences, such as approvals or financial transactions, stop and route to a named person before they happen.
Deploy with logging
Every input, output, and human override is written down, so anyone can reconstruct why the agent did what it did.
Monitor and improve
Watch the agent in production, review what it got wrong, and feed those cases back into the tests for the next release. How post-launch support runs is scoped per engagement.
How Code and Trust builds AI agents for clients
Code and Trust builds AI agents as scoped software, not experiments. Before the build we agree in writing which actions the agent may take unattended and which stop for a named person, the accuracy threshold it has to clear, where its outputs are logged, and which data may enter a prompt. Each agent is evaluated against your actual data before go-live, and if it misses the threshold, we do not deploy it.
Agents are built on top of the systems you already run. If a system has an API or a database we can reach, an agent can read from it and write results back. Human approval points are agreed per engagement: anything with significant downstream consequences, such as approvals or financial transactions, gets a human-in-the-loop step by default.
Most large builds are fixed-price, with a monthly retainer after launch. Builds start from $15,000, with the scope, price, and milestones agreed in writing before any code is written. You own what we build. For the wider engagement around agents, see AI implementation and AI workflow automation.
Agent factory vs. software factory
A software factory ships software. An agent factory ships AI agents. The two share the same discipline: standard parts, testing before release, and controlled deployment. Code and Trust uses agents inside its own software factory, and builds agents for clients through an agent factory.
For how we build and ship software itself, see our software factory.
Agent factory: common questions
Agent factory questions usually come down to what the term means, how agents are tested, whether they act without a person, and what it costs. Code and Trust tests every agent against your data before launch and agrees the human approval points with you in writing.
What is an agent factory?
An agent factory is a repeatable system for producing AI agents. Instead of building each agent from scratch, it reuses shared components (model access, tools and integrations, data access, and guardrails), tests every agent the same way before release, and deploys, monitors, and governs them under one set of rules. The point is agents that reach production and stay reliable there, rather than one-off experiments.
How is an agent factory different from building one AI agent?
Building one AI agent solves one problem once. An agent factory is the process and the shared parts behind it: the same way of choosing a workflow, defining what the agent may do, testing it, approving risky actions, and watching it after launch. The second agent reuses what the first one built, and every agent is held to the same standard.
How do you test an AI agent before launch?
We evaluate each agent against your actual data before go-live, using test cases drawn from the workflow it will run, with accuracy thresholds agreed upfront. If the agent doesn't hit the threshold, we don't deploy it.
Do AI agents act without human approval?
Only where you have agreed they may. Before the build we write down which actions the agent may take unattended and which stop and route to a named person first. Anything with significant downstream consequences, such as approvals or financial transactions, gets a human-in-the-loop step by default. The approval points are agreed per engagement.
What does it cost to build AI agents?
It depends on the workflow, the systems the agent has to reach, and how many actions need human approval. Most large builds are fixed-price, with a monthly retainer after launch. Builds start from $15,000, and retainers start from $1,750/month. The scope, price, and milestones are agreed in writing before any code is written.
Do we need to replace our current software to use AI agents?
Usually no. An agent works through the tools it is given, reading from your existing systems and writing results back. If your system has an API or a database we can reach, an agent can be built on top of it.
Related services
Related services cover the work around an agent. AI implementation fits agents into your operations, starting with a workflow audit. AI workflow automation builds the pipelines agents often run inside. The software factory is how Code and Trust builds and ships software.
Want AI agents that make it to production?
Tell us which workflow you want an agent to run. We will tell you whether an agent is the right fit, what it would be allowed to do on its own, and where a person would stay in the loop.