AI agent development company: systems that do the work
Tool-calling agents with typed tools, guardrails and traces — built so operations teams can trust the output and finance can predict the bill.
An agent is only useful when it is boring: predictable cost, observable steps, and a clear rule for when a human takes over. That is the part I build. The prompt is the last five percent.
What the build includes
- Graph design: nodes, state schema, termination conditions and failure paths.
- Typed tools wrapping your APIs, optionally exposed as an MCP tool server.
- Guardrails: input validation, output contracts, allow-lists and budget ceilings.
- Human-in-the-loop checkpoints for irreversible actions.
- Full run tracing, replay and regression evals on a fixed scenario set.
- Deploy on serverless infrastructure with queues for long-running work.
Frequently asked
What is an AI agent, in engineering terms?
A state machine where a language model chooses the next tool call, with typed inputs and outputs, retries, budget limits and a persisted trace of every step. The model is one component; the reliability comes from the surrounding graph.
When is an agent the wrong choice?
When the workflow is deterministic. If the steps never change, a plain pipeline with one model call per step is cheaper, faster and far easier to debug. Agents earn their cost when the path genuinely varies per input.
How do you keep agents from looping or burning tokens?
Hard step limits, per-run token and dollar budgets, idempotent tools, and a supervisor node that halts on repeated states. Every run is traced, so a runaway is visible in monitoring within minutes rather than at the end of the month.
Can agents work with my existing systems?
Yes. Tools are thin, typed wrappers around your existing APIs and database, often exposed over MCP so the same tool server works across models and clients. No system is rewritten to accommodate the agent.
Related reading
Want a scoped quote? Email daniyal@agenticcore.tech — I reply within one business day.