AI Agents
An AI agent is a system that uses a language model to plan and take actions — calling tools, querying systems, completing multi-step tasks — rather than only answering a question. Enterprise agents need tool integration, guardrails, evaluation, and human-in-the-loop controls to be trusted in production.
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Agents turn an LLM from a text generator into a system that gets work done: it reasons about a goal, calls tools, observes results, and continues. The hard part is not the model — it's the tooling, guardrails, and evaluation around it.
What a production agent needs
- Tools — typed, permissioned access to your APIs and data (the Model Context Protocol standardises this).
- Guardrails — input/output validation, allow-lists, and spend/rate limits.
- Evaluation — automated checks on real tasks before and after each change.
- Human-in-the-loop — approval gates for high-consequence actions.
Where agents earn their keep
Support triage, operations automation, data processing, and internal copilots — anywhere a defined multi-step process can be delegated with oversight.
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Proof it works
A platform for building and running autonomous AI agents that act across support, operations, and sales — not just answer question...
Read the case study AI InfrastructureA control plane for coordinating multiple AI agents and models across a single workflow, so teams compose automations instead of w...
Read the case study Cloud InfrastructureA control panel for provisioning and running virtual private servers, with an AI ops agent to help with routine operational tasks.
Read the case study