The AI agent development company built for agents that take real action.

A chatbot generates replies. An agent plans and takes actions: it calls APIs, queries data and triggers workflows to get something done, then reports back. That power is also the risk, which is why we build guardrails, scoped permissions and human checkpoints before we ship.

We start from the goal and the data, define the tools the agent may call, then build the loop: plan, act through a tool, observe, repeat, under scoped permissions. We build agents from scratch in Python on LangGraph or CrewAI with FastAPI and vLLM, or extend an existing codebase. We write an eval set before the build, track cost and task-success in CI, and add multi-agent coordination only if one agent cannot do the job. It ships through a staged rollout with a rollback path.

Scoped permissions for every tool the agent can touch, approval gates for high-impact actions, a policy layer that blocks anything out of scope, and a full audit log. Each action is observable, and there is always a safe fallback and a rollback path.

Our median time to a first production AI deployment is about 90 days. A scoped single-agent workflow can be faster; a multi-agent system with deep integrations takes longer. The timeline depends on data readiness, the tools the agent must use and your risk review.

A fixed-scope agent assessment runs roughly $20-40k over 2-3 weeks. A milestone build runs roughly $90-180k over 8-14 weeks with a small pod. A dedicated agent team runs $50-150k per month. Cost per run is one of the five numbers we agree up front, and our global delivery model gives you senior talent at a fraction of comparable onshore cost.

Yes, with planner and worker agents that coordinate through shared state and a message queue. We add the coordination only when one agent genuinely cannot do the job, because every extra agent adds cost and failure modes to manage.

We map the tool to the job rather than to a trend. Orchestration with LangGraph or CrewAI, retrieval with pgvector and rerankers, evals with an eval harness and LangSmith, tracing with OpenTelemetry, serving with vLLM and FastAPI. Most of our agent work is in Python. We use frontier models from OpenAI, Anthropic and Google, plus open-weight Llama or Mistral when on-prem or cost rules it.

An eval suite written before the build: reference cases, adversarial cases and regression tests, run in CI on every change. Combined with tracing in production, the agent's accuracy and cost are numbers you can watch on every change.

Ask how they measure success before they build, what guardrails wrap each action, and what happens when a step fails. A serious AI agent development company will name the numbers it commits to, show you an eval set, and put a senior engineer on the call. If the answer is a demo with no plan for production, that is the gap most projects fall into.

Yes. About a third of our AI engagements start there. We assess what exists, set the five numbers that were never defined, and rebuild only what blocks production, then give you a fixed scope to live.