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Agentic AI

Autonomous AI agents that do the work.

Tool-using, multi-step AI agents that reason, plan, and execute against real business workflows — with guardrails, memory, and human-in-the-loop checkpoints.

We've shipped 25+ AI products to production — so our agents come with the guardrails, evals, and audit trails most teams only add after their first incident.

The work

Agentic AI

Most 'AI features' stop at a chat box. Agentic systems go further: they take a goal, break it into steps, call the right tools, and carry a task through to a finished outcome — drafting the response, updating the record, escalating the edge case.

We build agents that are safe to put in front of a workflow: scoped permissions, audit trails, evaluation on your own data, and a human checkpoint wherever the cost of being wrong is high.

What we deliver

Scoped, shipped, and supported.

Tool-using agents with policies

Agents that call your APIs, search your knowledge, and act in your systems — inside permission boundaries you define.

Long-horizon planning and memory

Multi-step task decomposition with persistent memory, so an agent can pick up context across a long-running job.

Guardrails and human-in-the-loop

Approval checkpoints, content filters, and fallbacks for the cases that genuinely need a person.

Evaluation and audit trails

Every decision logged and scored against your metric, so you can prove the system behaves before it scales.

Typical stack
  • OpenAI
  • Anthropic
  • LangGraph / orchestration
  • RAG
  • Vector stores
  • Eval harnesses
Related work
Retail · BIBA

GenAI competitor-analysis product

A GenAI system that monitors competitor pricing, assortment, and positioning across channels — turning multi-source data into merchandising decisions.

Read the case study
FAQ

Questions we get a lot.

How is an AI agent different from a chatbot?

A chatbot answers a question. An agent takes a goal, breaks it into steps, calls the tools it needs, and carries the task to a finished outcome — updating records, drafting responses, and escalating the edge cases.

How do you keep agents safe in production?

Scoped permissions, content and action guardrails, human-in-the-loop checkpoints wherever the cost of being wrong is high, and full audit trails so every decision is logged and reviewable.

What does a first agentic project usually look like?

We scope one high-value workflow, build it against your data with an evaluation harness, and put a working agent in your hands with clear metrics — then expand once it's proven.

Let's build

Let's build what's next — together.

Whether you have a brief or just an idea, we'll help you scope a path from concept to production — with compliance built in.

Schedule a discovery call