Up nextNext in Building with AI Systems
AI Workflow Automation
Applications
Applications GUIDE
AI Agents
An AI agent is a system that uses observations and a goal to choose actions, often through tools, and then evaluates what happened.
On this page2 min read
Overview
Products use the term differently. The practical questions are what the system can do, under whose authority, and how completion is verified.
Key takeaways
- Specify authority and stopping conditions.
- Treat external instructions as untrusted content.
- Verify final state and disclose partial completion.
Deep Dive
A typical agent loop observes the current state, selects an action, receives a result, and decides whether to continue. The model may participate in planning or action selection, while ordinary software enforces permissions, budgets, and tool contracts.
Define the stopping conditions before execution. A task can be complete, blocked, cancelled, or only partially achieved. Repeated attempts without new evidence can waste resources or repeat harmful side effects. Limit action count, elapsed time, and spending where relevant.
External content can contain instructions that conflict with the user’s goal. Treat pages, messages, and tool responses according to their trust level. A document describing an action does not grant permission to carry it out.
Evaluate real outcomes. For a file-editing agent, inspect the final files and run appropriate checks. For an account workflow, verify the intended account and state. Record enough evidence to explain what changed and what remains uncertain. More autonomy increases the importance of clear boundaries and recovery procedures.
04Worked example
Define completion before acting
Suppose an agent must create a draft event for Tuesday at 2 p.m. in a specified calendar.
The postconditions include the correct calendar, date, time zone, title, and draft state. A successful tool response alone is not enough if it saved to another calendar.
Read the resulting record and report any mismatch before declaring the task complete.
What it shows
The invented workflow demonstrates outcome-based verification.
Strategic Impact
Build choices
Application-level design determines whether AI improves real outcomes.
Team and workflow
Good workflow integration creates productivity gains users can trust.
Risk and safety
Well-scoped use cases reduce change fatigue and implementation risk.
Real-World Implementation
Repair a failing test, then rerun it and inspect the change.
Collect authorized records and produce a report with traceable sources.
Risks & Guardrails
Automating a broken process can amplify existing problems.
Teams may over-automate and remove needed human judgment.
Quality can drift if outputs are not continuously evaluated.
Implementation Roadmap
Map the current workflow and identify the highest-friction step.
Define human checkpoints before full automation.
Train users on prompts, escalation paths, and quality standards.
Track task-level outcomes to confirm sustained value.
Sources and further reading
Keep Exploring
Free newsletter
Keep up with AI in 3 minutes a day
One short email each weekday with the three AI stories that actually matter. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the AI Agents quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Frequently asked questions
Does an agent need unrestricted access?
No. Narrow tools and permissions can support useful work while limiting the consequences of mistakes.
Keep learning
Related guides
More guides picked for this topic