AI Agent Development
Build AI agents that can actually work inside your business. We build agents that use tools, memory, APIs, and your business systems, with clear permissions, testing, activity logs, and human approval. So an agent that works in a demo keeps working on real requests and edge cases, not just the ones you tested.
Need GPU hosting or an AI platform for the whole company instead of one agent? That's AI Platform Engineering, not this. Already live and need to know the next release won't make it worse? See AI Evaluation.
Built for Real Work, Not Agent Demos
A tool-calling loop that works in a demo isn't a production agent. We build the permissions, memory, testing, and human oversight around it so it keeps making the right call on real requests and edge cases, not just the ones you tested.
What is AI agent development?
AI agent development is designing, building, and running AI systems that work through multi-step tasks on their own: calling tools and APIs, remembering context, and acting toward a goal, with clear permissions, testing, and human oversight. It's different from a chatbot, which only replies. Some teams search for this as custom AI agents or agent engineering.
Tools We Use
We pick the right tool for the job, not a favorite: LangGraph, Temporal, and the major model providers, chosen for what the work needs.
Recognize the symptoms
When You Need AI Agent Development
If any of these are happening, your agent probably needs an engineering review.
- The agent makes different decisions on the same input from one run to the next
- Tool calls fail unpredictably: bad inputs, the wrong tool picked, no retry
- The conversation grows until the agent loses track of the task
- Testing is weak: no benchmark, no regression checks, no way to show it's reliable before a change ships
- There's no rollback when a bad prompt or tool change reaches production
- Nobody can say which tools an agent can call, or on what data
A tool-calling loop in a notebook isn't a production agent.
- AI agent development covers systems where the model decides what to do next: working toward a goal, calling tools, keeping track of context, bringing in a person when needed, and stopping safely. That's what makes it an agent instead of a chatbot with extra steps.
- The hard part in production usually isn't the model's reasoning. It's everything built around it: what tools it can call, what it remembers, how people approve its actions, how you test it, and what stops it from making the same mistake twice.
- We break the workflow into steps, define exactly what each tool can and can't do, and choose how the pieces get coordinated based on what the work needs.
- We build the memory and approval steps, put together a test set, and show it's reliable against that test set before giving it more independence.
What We Build
Eight parts that work together, organized around what the agent needs to run reliably, not which framework built it.
Building a single agent from start to finish has its own page: The agent build in detail.
Agent design
How the agent decides what to do next, where its decisions stop, and how it knows it's done, instead of just answering one prompt.
Coordination
How a task gets split up and handed off: one step at a time, several at once, or one agent directing others. Chosen for how the work actually breaks down, not a framework default.
Tool use
Rules for which tools and APIs the agent can call, with what data, and what it must never touch. This is the integration work that makes tool-calling agents reliable, not just fast.
Memory
What it remembers within one conversation and over time, kept strictly separate between customers so nothing leaks across accounts.
Saved progress
Saving its place so it can pick up where it left off instead of starting over every time it's interrupted.
Planning
Breaking a task into steps as it goes when they can't be predicted in advance, with something coordinating the work.
Human approval
Checkpoints where a person approves risky actions, gets alerted when the agent isn't confident, or reviews and fixes its work before it continues.
Multi-agent systems
Splitting the work across several specialized agents with a coordinator, once one agent's job gets too complex to run reliably by itself.
Running your own model infrastructure or building an AI platform for the whole company is a different service. See AI Platform Engineering. Compliance work for SOC 2 or the EU AI Act is part of AI Governance.
One agent that works in a demo is no longer enough.
People now say agentic AI instead of agent, and talk about testing and operating agents, because what production needs has changed.
- A single agent calling a couple of tools is easy to prototype. Agentic AI, with several agents coordinating, deciding when to hand off, and sharing memory, is a different engineering problem. It's the one most enterprise agents face once one agent's job outgrows a single loop.
- Testing and operating agents are now their own disciplines, not one step in a build, because an agent's failure isn't an error message. It's a wrong decision that looked reasonable at the time. Without testing and monitoring from day one, an agentic system gets worse quietly.
- That's why we treat custom agent development as systems engineering, not prompt writing: coordination, tool use, memory, and testing are designed together, so the system still makes the right call after the twentieth edge case, not just the first ten.
What a Production Agent Needs
Eight parts that work together, not eight separate scripts that don't talk to each other.
Decision loop
A model that decides what to do next on its own instead of just replying to one prompt, with a built-in stopping point so it can't loop forever.
Tool access
Tools it can call to look things up or take action, each locked down to exactly what it's allowed to do.
Permissions and guardrails
Clear rules for what the agent can and can't do, enforced in code, not just described in a document.
Memory
Memory of the current task, plus memory that carries over time, kept separate for each customer and account so nothing leaks across them.
Human approval
A way for people to approve, pause, or fix its actions before they go through, or take over when it gets stuck.
Testing
A test set that defines success, failure, and what it must never do, checked before every change ships.
A record of every action
A log of every decision and action, so finding out what went wrong is a lookup, not a guess.
Safe rollout
Controls for launching safely: test quietly first, limit what it can do, and roll back fast if something breaks.
Agents We Build
Organized around what the agent is used for, not the framework underneath.
Support and service agents
Agents that look up orders, accounts, and tickets and act on them. Actions that can't be undone need approval until testing shows the risk is understood.
Research and analysis agents
Agents that pull from several sources, combine the findings, and cite where each claim came from, instead of giving an answer nobody can trace.
Operations agents
Agents that watch internal systems and take routine action, like flagging, routing, or updating records, with tight tool permissions and a full audit trail.
Coding agents
Agents that read, write, and test code against your own repo and standards, with human review on anything that touches production.
Multi-agent workflows
Several specialized agents working on one task under a coordinator, once one agent's job has grown too big for a single loop.
How Agents Are Coordinated
Chosen for how the work actually breaks down, not a framework default.
One agent with tools
One model working step by step, calling tools and reacting to what comes back. The right starting point for a clear task with a handful of tools.
Coordinator and workers
A coordinating agent breaks the task apart and hands pieces to specialized worker agents. Used once one agent's job gets too complex to run reliably alone.
Step-by-step handoff
A fixed chain where each agent finishes its step and passes to the next. Chosen when the steps are predictable and don't need a coordinator watching everything.
Human approval checkpoints
Set pause points where a person must approve before the agent continues. Used for any action that's expensive, risky, or hard to undo.
What You Receive
AI Agent Development vs Chatbot Development
A chatbot replies to one message at a time, with no memory of what it decided to do or why. An agent decides what to do next on its own, calls tools, keeps track of context across steps, and knows when to stop or hand off to a person. Building an agent means designing the permissions, memory, and testing a reply-only chatbot never needed.
AI Agent Development vs RPA
RPA (robotic process automation) follows a fixed script against a stable screen or API. It breaks the moment the input doesn't match what it expected. An agent works out what to do next from unstructured input, adapts when a step fails, and calls tools instead of clicking through a recorded macro. The two often work together: RPA for a predictable back-office step, an agent for the parts that need judgment.
From our experts.
Writing on agent reliability, rollout, and observability.
Our AI Agent Development Process
The same nine-phase process we use everywhere, with a checkpoint at every step. For an agent, testing covers whether it makes the right calls, whether its tools work correctly, and whether it makes things up.
Related Engineering Services
Common questions.
Bring us the agent that isn't a system yet.
Whether it's a prototype that needs permissions and testing, or something already live and making the wrong call without anyone noticing, we'll look at where it stands before recommending anything.
No hype · No forced roadmap · Just a clear view of what the system needs next