What are AI agents? Autonomous AI in business workflows

AI agents go beyond assistants that just answer questions: they plan multiple steps towards a goal and carry them out using various tools. For example, an agent can read an email, check the calendar, find a free slot and draft a meeting invite.

How they differ from chat assistants

• Tool use: searching, querying databases, calling APIs, creating files
• Planning: breaking a goal into subtasks and ordering them
• Loop: evaluating results and taking another step if needed
• Memory: carrying information across steps and tasks

Use cases

• Classifying a customer request, opening a ticket and drafting a reply
• Pre-sales research: gathering and summarising company information; see AI in sales and marketing
• Reading invoices and entering them into accounting
• Debugging and writing tests in software; see AI in software development
• Reporting: collecting data from sources into a weekly summary

Design principles

• Start narrow: choose a well-defined, measurable, repetitive workflow.
• Human approval: require approval before irreversible actions such as payments, customer messages or deleting data.
• Least privilege: give the agent access only to the tools and data its task needs.
• Transparency: log the agent's steps and show them to users.

Risks

• Compounding errors: an early wrong step affects later ones.
• Prompt injection: malicious instructions in a document or web page can steer the agent; see our AI security guide.
• Cost: multi-step tasks mean more model calls; see AI costs.

Measurement

Track task success rate, share of steps needing human help, time and cost; see our model evaluation guide.

Conclusion

Agents promise big efficiency gains in repetitive workflows. The keys are narrow scope, strong safeguards and human approval at critical points.