AI Agents
Autonomous and human-in-the-loop agents wired into your real tools and data, built with guardrails so they stay inside their lane.
For teams whose work spans documents, business tools, and repeated decisions. We build AI agents that retrieve information and take scoped actions, with evaluation and human review designed into the workflow.
Illustrative example of a delivery environment. Figures shown are representative, not client or production data.
What's included
AI Agents, broken down
LLM applications
Product features powered by language models, wired into your existing data and workflows — not a bolted-on chatbot.
RAG systems
Retrieval pipelines that ground model answers in your own documents and data instead of hallucinated guesses.
AI agents
Multi-step agents that call your tools and APIs, with the guardrails to keep them inside their lane.
Evaluation & monitoring
Automated evals and production monitoring so model behavior gets caught drifting before your users do.
Outcomes
What changes when we run this line
Scoped actions, not open-ended autonomy
Tool permissions, approval steps, and fallback paths are agreed during scoping, so the agent acts inside limits your team has signed off.
Evaluated before release
Evaluation criteria are set up front and checked before each release, so behavior is tested rather than assumed.
Observable after launch
Monitoring on failures, latency, and cost gives your team what it needs to review the agent and improve it.
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Related service lines
FAQ
Common questions
What is the difference between an AI agent and a chatbot?
A chatbot primarily answers questions. An AI agent can take multi-step action: looking up information, calling tools or APIs, and completing a task, with a person able to review or approve along the way.
How long does it take to build a production AI agent?
Timing depends on the workflow, integrations, data access, and review requirements. We scope a focused first workflow with you, agree on evaluation criteria, and estimate a pilot before committing to a production rollout.
How is our data handled in an AI project?
Data handling is agreed in writing before build. During scoping we settle which model provider and deployment option is used and what its retention and training terms are, what personal or sensitive data may reach a model, which tools the agent can call, where a person approves an action, and what is logged for audit. We can work with the providers and environments your security team already approves.
How do you control what an agent can do?
We define tool permissions, approval steps, and fallback paths during scoping. Evaluations check expected behavior before release, and monitoring helps your team review failures, latency, and cost after launch.
Have a product to build, scale, or improve?
Tell us what you’re working on. We’ll bring the right mix of software engineering, AI, QA, cloud, and product expertise to move it forward.