AI agent development
An AI agent is a language model given tools and a goal, allowed to decide which tools to call and in what order. Building one that demos well takes a week. Building one you’d let touch a production system takes considerably longer, and the difference is almost entirely error handling.
What we build
Internal operations agents. Triage tickets, reconcile records across systems, draft responses from your own documentation, escalate what they can’t resolve.
Data and research agents. Query warehouses, pull from APIs, compile structured findings. Common in analytics teams that are drowning in ad-hoc requests.
Workflow agents. Multi-step processes that previously required a person to move information between four systems that don’t talk to each other.
Customer-facing agents. Higher bar, tighter guardrails, more evaluation. Worth building only when the internal versions have already proven the tooling.
The decision most teams get wrong
Not every problem needs an agent. If the sequence of steps is known in advance, write the sequence. A deterministic pipeline that calls a model at two specific points is cheaper, faster, easier to debug and dramatically more reliable than an agent deciding the same steps at runtime.
Agents earn their complexity when the path varies by input, when step three depends on what step two returned and you can’t enumerate the branches. That category exists, and it’s smaller than the current market noise suggests.
We will tell you when a workflow beats an agent. It’s a shorter engagement for us and a better system for you.
What makes agents hard
Tool design. Agents fail more often because a tool returned an ambiguous result than because the model reasoned badly. Tools need narrow contracts, explicit errors, and descriptions written for a reader with no context.
Loop control. An unbounded agent will retry the same failing call until you stop paying for it. Step caps, cost ceilings and progress checks are not optional extras.
State and recovery. When an agent fails at step seven of nine, what happens? Systems that can’t resume from a checkpoint end up re-running expensive work or, worse, repeating side effects that were never meant to happen twice.
Evaluation. You need a fixed set of scenarios with known-good outcomes, run on every change. Agents fail non-deterministically, so a single successful test run tells you almost nothing.
Permissions. An agent with write access does what it was persuaded to do, not what you intended. Scope credentials narrowly, require confirmation for irreversible actions, and log every call.
How the engagement works
Senior engineers with production agent work in recent history, embedded in your team on time and material. No fixed scope: agent projects change direction in the first fortnight almost without exception, and a fixed-price contract turns that into a negotiation instead of an engineering call.
Engineers start within 14 days. Rates by role and market are on the daily rates benchmark.
Go deeper
- AI agents explained — architectures, patterns and terminology
- What is RAG? — retrieval, usually an agent’s most-used tool
- AI consulting services — start with feasibility if the problem is unscoped
- Hire AI engineers — embed agent engineers directly
- AI chatbot development — the conversational case specifically
Have a workflow you think an agent could run?
Describe it on a free 30-minute call. You get an honest answer on whether it needs an agent or a pipeline, what the tooling layer would involve, and a T&M cost range either way.
staffai.eu · Senior AI and data engineers from Eastern Europe, on T&M