Autonomous AI agents that reason, plan, and execute complex workflows.
Design and deploy teams of specialised agents that collaborate, delegate tasks, and share context to solve complex workflows.
Agents that autonomously interact with APIs, databases, file systems, browsers, and third-party services.
Advanced chain-of-thought and tree-of-thought reasoning for agents that break down complex problems into executable steps.
Persistent memory systems that let agents learn from past interactions, maintain context, and improve over time.
Configurable safety boundaries, approval workflows, audit trails, and rollback capabilities for controlled autonomy.
Agents that detect failures, retry with alternative strategies, and escalate to humans when confidence is low.
An agent reasons, plans and executes workflows rather than only answering. That means tool access, a defined authority boundary, and the ability to act unattended within it — which is why the engineering effort concentrates on permissions, failure handling and observability rather than on the model.
Approval gates live in the execution layer, not the prompt. High-impact actions cannot execute without explicit approval regardless of what the agent decided, and every step is logged so the decision can be replayed. Prompt-level restraint is a preference; a gate is a control.
Start with the least that solves the problem and increase it as the system earns a track record. Human-in-the-loop is not a failure state — it is how an agent accumulates the evidence needed to be trusted with more. Projects that begin at full autonomy tend to stall at the pilot boundary.
That case is designed for from the first commit, because it is routine rather than exceptional. Depending on the workflow we use idempotent actions with retry, checkpoint-and-resume, or explicit compensating actions — so a half-completed sequence never leaves the system in a state nobody designed.