What you will learn
You will be able to trace an agent from a user goal to a checked outcome, explain what “reasoning” means without treating it as magic, and identify the points where the agent should continue, retry, stop or ask a person.
The 60-second explanation
An AI agent works in a loop. It receives a goal, examines the information available, chooses a suitable next step, uses an authorised tool, observes the result and decides what to do next. The loop ends when the completion criteria are satisfied or a stopping rule is reached.
This is different from a fixed automation. A traditional workflow may always execute steps A, B and C. An agent may choose B after A, retry A when data is missing, or ask a person when the result creates risk.
The agent is not “thinking like a human.” It is using a model plus application rules and current evidence to select an action. Reliable agent design makes this process constrained and testable.
Rahul follows the agent’s decision loop
Goal: prepare a customer meeting brief from approved project notes.
One request, six responsibilities
Consider: “Prepare tomorrow’s customer meeting brief using our approved project information.”
| Responsibility | Question the system must answer | Example |
|---|---|---|
| Goal | What outcome is expected? | A concise meeting brief |
| Context | What limits and facts matter? | Customer name, meeting time, approved sources |
| Reasoning | What is the best next step now? | Find the latest status update first |
| Tool | Which capability can perform it? | Search project documents |
| Action | What operation should run? | Read the latest authorised document |
| Check | Is the result complete and acceptable? | Agenda, open issues and decisions are present |
If no approved source contains the latest status, the agent should not invent it. It should mark the gap and request help.
Goal: define the outcome, not a vague activity
“Help with my meeting” is too broad. A useful goal states the deliverable, boundaries and definition of done:
Prepare a one-page brief for tomorrow’s Acme review. Use only the approved account notes and project-status folder. Include the meeting objective, three recent developments, open risks and decisions required. Cite every source. Do not send the brief.
The goal gives the agent room to organise work but removes unsafe ambiguity. It also makes evaluation possible.
Reasoning: choosing the next useful step
In agent systems, reasoning means using the current goal and observations to select the next action. It may include:
- breaking a large outcome into smaller tasks;
- selecting the right source or tool;
- comparing a result with the completion criteria;
- recovering from an expected failure;
- deciding that human input is required.
Reasoning is not automatically reliable. The application should constrain the available actions, validate tool inputs and check results. For sensitive workflows, store a short action rationale rather than exposing or depending on hidden model reasoning.
Tools and actions are not the same
A tool is a capability made available to the agent. An action is one specific use of that capability.
| Tool | Possible read action | Possible write action |
|---|---|---|
| Calendar | Check availability | Create or cancel an event |
| Read an approved thread | Send a message | |
| Document service | Search policies | Edit or delete a file |
| CRM | Read account status | Change an opportunity stage |
Read and write operations must not share the same approval assumption. An agent allowed to inspect a calendar is not automatically allowed to book a meeting.
Observation turns an action into a loop
After every tool call, the agent receives an observation: results, an error, a confirmation or an empty response. That observation changes the next step.
Appointment scheduling with changing information
The goal is to find a 30-minute appointment this week. The first calendar search returns no common slot. A good agent does not repeatedly run the same search. It may check whether the user allowed a wider time window, suggest two alternatives, or ask which constraint can change. If a slot becomes unavailable before confirmation, it must recheck rather than send an outdated option.
Simple Tanglish: Result கிடைக்கவில்லை என்றால் agent guess பண்ணக் கூடாது. Constraint மாற்றலாமா என்று கேட்க வேண்டும்.
Completion and stopping rules
An agent needs both a definition of done and reasons to stop.
Completion criteria for the meeting brief could be:
- the correct customer and meeting date are confirmed;
- every required section is present;
- factual claims are linked to approved sources;
- missing information is visibly labelled;
- the draft is saved but not sent.
Stopping rules could be:
- a required source is unavailable;
- the user’s identity or permission cannot be verified;
- tool calls fail repeatedly;
- sources contradict each other;
- the next action would send, pay, delete or change access without approval.
Prompt: turn a request into an agent workflow
Convert the work request below into a safe beginner-friendly AI agent workflow.
Work request: [paste the request]
Return:
- Clear goal and definition of done
- Required information
- Steps the agent may choose
- Tool needed for each step
- Expected observation after each action
- Retry limit
- Human approval points
- Stop-and-ask conditions
Do not assume access to any system that I have not explicitly authorised.
Common mistakes
Common mistakes
- Giving the agent a task but no completion criteria.
- Treating the first generated plan as fixed even when observations change.
- Allowing a tool call without validating its inputs and output.
- Retrying forever when the source or service is unavailable.
- Confusing a fluent explanation with evidence that an action succeeded.
- Reporting “done” before checking the real external system.
Interview or discussion bit
What is the basic AI agent loop?
Strong answer: An agent receives a goal, uses the current context to choose a next step, calls an authorised tool, observes the result and checks progress. It repeats this loop until the completion criteria are satisfied or a stopping rule requires human help.
Key takeaways
Key takeaways
- A clear goal includes boundaries and a definition of done.
- Reasoning selects the next step from current evidence; it is not magic.
- A tool is a capability, while an action is a specific tool operation.
- Observations determine whether the agent continues, retries or changes direction.
- Every workflow needs completion checks, retry limits and stop rules.
Frequently asked questions
Does an agent create one complete plan before acting? Not always. Many agents choose a next step, observe the tool result and revise the plan as new information appears.
What makes a goal usable? A usable goal defines the desired outcome, permitted resources, important constraints and a clear completion check.
Why must an agent inspect tool results? A successful tool call only proves that the call ran. The result must still be checked for correctness, completeness and safety.
Interactive Knowledge Check
Choose an answer, inspect the explanation and explain the idea in your own words.