AI agent development

What goes into building an AI agent

The language model is the part everyone talks about, but it is a small share of the work. A model on its own can only read and write text. Turning it into an agent that does a job reliably means building everything around it. A custom AI agent usually has six parts:

• Instructions: a written brief that tells the agent its role, its tone, what it may do and what it must hand to a person.
• Tools: small, well-defined actions the agent can call, such as looking up an order, creating a task or sending a message.
• Memory: what the agent remembers within a conversation, across conversations, and where it looks up facts about your business.
• Guardrails: hard limits enforced in code, not just in the instructions, such as spending caps, approval steps and blocked actions.
• Evals: a test set of real examples the agent is checked against every time something changes.
• Hosting and monitoring: the servers, logs, alerts and dashboards that keep it running and show you what it did.

Tools: how an agent acts in your systems

Tools are where most of the engineering goes. Each one is a narrow piece of code with a clear job, clear inputs and a clear result. Instead of giving an agent open access to your database, we give it something like “find customer by phone number” or “add a note to this job”. That keeps its reach small and its behavior predictable.

Take a property management company that wants an agent to handle maintenance requests. Its tools might be: look up the tenant and unit, check open work orders, create a new work order, check a vendor’s availability, and text the tenant an update. The agent cannot change rent, delete records or contact vendors outside the approved list, because those tools simply do not exist. Read tools and write tools are kept separate, and write tools can require a person to approve before they run.

Memory, guardrails and evals

Memory decides how useful an agent feels. Short-term memory keeps track of the current conversation or task. Longer-term memory might store a customer’s preferences or the history of a case. Reference memory, often built with retrieval over your documents, lets the agent look up your policies and prices rather than relying on what the model learned in training. We choose what to store, for how long, and who can see it, with privacy in mind from day one.

Guardrails and evals are what separate a demo from something you can trust with customers. Guardrails stop the agent from taking actions it should not, whatever it is told. Evals are a library of real cases from your business, including the awkward ones, that we run the agent against before every release. When a model provider ships a new version or we change the instructions, the evals show whether the agent got better or worse. Agents still make mistakes, so every action is logged and anything high-stakes can wait for a person.

Frameworks, builders and a custom build

You have options. Custom GPTs in ChatGPT and Claude projects are a quick way to try an assistant on your own documents, but they live inside someone else’s app and cannot reliably act in your systems on a schedule. No-code agent builders such as Microsoft Copilot Studio or Zapier’s agents can connect to common apps and suit simple, well-supported tasks. Developer frameworks and SDKs from the model providers and open-source projects like LangGraph give an engineering team building blocks, but someone still has to design, build, test and run the agent.

A custom build makes sense when the agent touches several systems, needs rules specific to your business, handles customer data, or has to keep working as models change. We work with providers such as OpenAI, Anthropic and Google, and with open-source models, and we design agents so the model can be swapped without rebuilding everything. If a builder tool covers your job well, we will tell you.

AI agent development, step by step

1. Map the jobWe write down every step, rule, exception and hand-off the agent will cover, with the people who do the work today.
2. Design tools and limitsWe list each tool, what it can read or change, and which actions need approval.
3. Build the eval setWe collect real past examples, including hard ones, and agree what a good result looks like for each.
4. Build in roundsYou review the agent working on your examples after each round, and we adjust instructions, tools and memory.
5. Launch with reviewThe agent goes live with people checking its work, then takes on more as results hold up.
6. Host, monitor and improveWe run it, watch the logs, re-test on model updates and keep making changes as your business changes.

What custom AI agent development costs

Zoti custom projects start at $499/month on a monthly plan that covers design, development, hosting and ongoing changes. The price depends on how many tools and systems the agent uses, how much memory and review tooling it needs, and how many tasks it covers. AI usage fees from the model provider depend on volume, and we explain them up front. You get a written quote before any work begins.