AI agents

AI agents for real operations, not just chat

Most 'AI agent' products stop at a chatbot that can answer questions but can't act. We build agents that read your data, reason about it, and execute real actions inside the systems you already run with guardrails and approval gates that make autonomy safe instead of risky.

Autonomous workflowsTool useGuardrails
Quick answer

AI agent development: agents that read data, make decisions, and take real actions inside your tools with guardrails, not just chat.

A chatbot that cannot act is not automation

Teams end up doing the real work by hand even after adding an AI layer, because the AI only observes and suggests a human still has to execute every action. The engineering work that turns 'AI that talks about your data' into 'AI that changes something in your system' is almost always skipped, and it's the part that actually saves time.

What agent builds include

  • Agents wired into your real tools via API ticketing systems, ad platforms, internal databases
  • Multi-step reasoning that decides what action to take, not just what to say
  • Approval workflows: recommend-only, approve-to-execute, or autonomous within limits
  • Full audit logs so every action an agent takes is traceable
  • Evaluation and monitoring so autonomy doesn't mean unpredictability

How we work

  1. Map the workflow end-to-end before touching a model where decisions get made, where it breaks

  2. Ground the agent in your real data via RAG, not general training knowledge

  3. Start in recommend-only mode so your team can grade the agent before it acts

  4. Expand autonomy gradually, guardrail by guardrail

Typical stack

Anthropic / OpenAI APIsLangChainRAG pipelinesVector databasesPython

Frequently asked questions

A chatbot answers questions. An agent decides what to do next and does it calling APIs, updating records, triggering workflows based on reasoning over your data and goals. The distinguishing engineering work is tool integration and guardrails, not the underlying language model.

Only with engineering discipline built in: hard limits enforced outside the agent, an allowed-action list, approval gates above defined thresholds, and full audit logs. We start every agent in recommend-only mode and expand autonomy only as its track record earns it.

High-frequency, well-defined decisions with a clear right answer are the best starting point the ones eating the most analyst hours today. Ambiguous, judgment-heavy decisions are better left with a human for now and revisited once the narrower agent has proven itself.

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