What an AI agent is
An agent is a language model given tools and a goal. Instead of returning text, it can plan steps, call functions you define — search a database, send a draft, update a record — check the result, and continue until the task is done. That makes it useful for workflows, not just answers.
The power is also the risk: a system taking actions can take wrong ones. Serious agent development is mostly about controlling what it can do, checking its work, and keeping a human in the loop for anything consequential.
- Uses tools you define to take real actions, not just generate text
- Plans and executes multi-step tasks, adapting as it goes
- Good fit: research-and-summarize, data lookup, routing, drafting workflows
- Each action is a defined, permissioned function — not open-ended system access
- Best where steps are repetitive but need judgment a fixed script can't handle
Guardrails and human oversight
We scope what an agent is allowed to do tightly, and we make consequential actions pass through checks or a human before they happen. Reading data can be automatic; sending an email, moving money, or deleting something should not be, and we build it that way.
We also make the agent's work visible: logging each step it takes and each tool it calls, so you can audit what it did and why. When it's unsure or hits its limits, it should stop and ask, not push forward — abstaining is a feature, not a failure.
- Least privilege: the agent can only call the specific tools you approve
- Human-in-the-loop approval for irreversible or high-stakes actions
- Step-by-step logging so every action is auditable
- Limits on loops, retries, and spend so it can't run away
- Clear failure behavior: stop and ask rather than guess when uncertain
- Sandboxed testing before it ever touches live systems
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Frequently asked questions
Isn't it risky to let AI take actions on its own?
It can be, which is why guardrails are the core of the work, not an afterthought. We limit what the agent can touch, require human approval for anything consequential, and log every step. Reversible, low-stakes actions run automatically; risky ones don't.
What tasks are agents actually good for right now?
Multi-step but bounded workflows — research and summarize, look up and route, draft and prepare — where each step is a defined tool call. They're less suited to fully open-ended goals with no oversight, and we'll be honest about where the line is for your use case.
How do we keep control?
Through least-privilege tool access, human approval gates on important actions, spending and loop limits, and full logging. You decide what runs automatically and what needs a person to sign off.