Building AI Agents for Business Workflows

An AI agent differs from a chatbot in one important way: it doesn't just generate text, it takes actions — calling APIs, updating records, triggering workflows. That capability is genuinely useful, and it raises the stakes on getting the design right, because a wrong answer from a chatbot is embarrassing while a wrong action from an agent can have real consequences.

Start with the failure mode, not the happy path

The first design question for any agent shouldn't be "what can it do" — it should be "what happens when it's uncertain, or wrong." Every action an agent can take needs an answer to: is this reversible? Does it need human confirmation first? What's logged if it goes wrong?

A practical design pattern: constrained tool access

• Give the agent a specific, limited set of tools/actions rather than open-ended system access.
• Require explicit confirmation for actions with real-world consequences (sending an email, modifying a record) versus read-only actions.
• Log every tool call with its reasoning, so a human can audit what happened and why.
• Design a clear escalation path for anything the agent can't confidently handle.

Where agents earn their complexity

Multi-step workflows that would otherwise require a human to gather information from several systems and make a judgment call are the clearest fit — qualifying an inbound lead by cross-referencing several data points, for instance, or triaging a support request against a knowledge base before routing it. A simple, single-step task rarely justifies agent complexity over a straightforward automation.

Evaluation doesn't stop at launch

Unlike a traditional automation script, an agent's behavior can drift as the underlying model changes or as inputs evolve. We treat agent evaluation as an ongoing part of Growth, not a one-time pre-launch check — the same way we'd monitor any other production system.