AI Agents Development

At its core an agent is a loop. The model reads the goal and current context, decides on the next step, calls a tool - a search, an API request, a database query, a browser action - reads the result, and repeats until the task is done or it needs a human. The quality of that loop depends less on the model than on the tools it is given: clear names, tight input schemas, useful error messages and results that are concise enough to reason over.

Not every workflow needs full autonomy. Where the steps are known in advance, a fixed pipeline with model calls at specific points is cheaper, faster and easier to test. We reserve open-ended planning for tasks where the path genuinely varies - research, triage, investigations - and often combine the two: a deterministic workflow that hands a bounded sub-task to an agent. Multi-agent setups, where a coordinator delegates to specialist agents, help when a job spans distinct skills or very large context, but they add cost and failure points, so we use them only when a single agent clearly struggles.

Agents connect to your systems through tools. The Model Context Protocol (MCP) has become a common standard for this: a system is exposed once as an MCP server and can then be used by different models and agent frameworks without bespoke glue code. We build MCP servers for internal APIs and data sources, or connect existing ones, and wrap them with the access rules that matter.

Permissions follow least privilege. Each agent gets its own scoped credentials, read access is separated from write access, and destructive or costly actions - issuing refunds, sending external emails, changing production data - require a human approval step. Code execution and browser automation run in sandboxed environments with limited network access. Every tool call is logged with its inputs and outputs, so you can see exactly what an agent did and why.

Agents fail differently from chatbots: they can pick the wrong tool, loop, misread a result, or reach the right answer by an unsafe route. So we evaluate whole runs, not just final answers. A test suite of realistic tasks is replayed against each new version, scoring task success, the steps taken, policy violations, cost and time per run.

Traces of production runs feed back into that suite. When an agent mishandles a case, the trace becomes a new test, so the same mistake is caught before the next release.

Production agents need limits. We set caps on steps, tool calls, tokens and wall-clock time per task, add timeouts and retries with backoff for flaky APIs, and make actions idempotent where possible so a retried step does not create a duplicate order or ticket. When an agent cannot finish, it stops cleanly, records what it completed, and escalates to a person with the context needed to take over.

Cost is managed by routing: smaller, faster models handle routine steps such as classification and extraction, larger models handle planning and hard reasoning, and repeated context is cached. Per-run cost is tracked alongside success rate, so you can see what each automated task costs to operate.