Mental BoundWhat is an AI agent?
An agent is an AI system that pursues a goal on its own: it breaks a task into steps, decides what to do next, calls external tools (databases, APIs, code interpreters, browsers), observes the results, and continues until the job is done. Where a standard LLM conversation is a single question and answer, an agent runs in a loop of reasoning, acting, and observing — that loop is what gives it autonomy.
Agents already handle real work: drafting and sending emails, writing and testing code, researching topics across many sources, managing support tickets, and orchestrating multi-step business processes.
Why do agents need guardrails?
The more autonomy an agent gets, the more important the guardrails: what it may and may not do, when it must ask for confirmation, and how its decisions are audited. Well-designed agents are transparent about their reasoning and defer to a human when needed.
Where does it show up in our work?
In client work an agent earns its autonomy one permission at a time. We start with read-only tools and a human approval step on anything that sends, pays or deletes, then widen the loop as the audit log shows the agent choosing well. That pattern is the core of our Cowork and agentic adoption service and of the agentic harness we build for teams running agents on their own systems. Our read of Anthropic's 2026 State of AI Agents report shows where most deployments actually stand.