You ask an AI tool to explain a software error, and it gives you an answer. Then you ask another system to investigate the error, edit the code and check the result. Both use AI, but the work they perform is different.
This is where the discussion about AI agents vs chatbots becomes useful. Understanding the distinction helps you choose a suitable tool for a real task instead of buying into a label.
What is an AI agent?
In this article, an AI agent means a system that uses an AI model to choose its next steps and invoke available tools while pursuing a goal. It can inspect a tool's result and adjust what it does next.
A workflow, by comparison, follows a predefined sequence. Anthropic makes this architectural distinction in its Building effective agents guide. Definitions vary across products, so check the actual behavior rather than relying on the word “agent.”
Is a chatbot different from an agent?
A chatbot describes a conversational interface. An agent describes how a system performs work. They are not mutually exclusive: a chat interface can connect to an agent, and a chatbot may also have tools.
The useful question is: does the system mainly respond, follow fixed steps, or choose additional actions based on what it discovers?
| Approach | Illustrative task | What to examine |
|---|---|---|
| Answer-focused assistant | Explain a SQL error | Accuracy and clarity of the answer |
| Fixed workflow | Extract invoice fields, validate them, then produce a report | Rules and checks at each stage |
| Agentic system | Investigate a failing test and adapt the repair after checking results | Available tools, allowed actions and stopping conditions |
A practical example: debugging an ASP.NET page
Imagine a TextChanged handler is not producing the expected result. The following is a hypothetical example, not a claim about a particular product.
An answer-focused assistant could explain AutoPostBack and suggest checking event wiring. A fixed workflow could inspect specified files and return a checklist. An agent with suitable permissions could choose which files to inspect, propose an edit, run available checks and revise the edit if a check fails.
The important outcome is a reviewable change supported by evidence. A confident explanation alone does not show that the application now works.
Three more examples you can evaluate
1. Preparing a blog article
Suppose you want a tutorial from a set of approved sources. A useful trial task would ask the system to produce an outline, cite the sources and mark unsupported claims. Compare the draft with your source material before publishing. A simpler assistant may already be enough.
2. Reviewing a spreadsheet import
For a file containing transaction records, define expected columns, date rules and duplicate criteria first. Test whether the system can explain rejected rows correctly. Keep totals and validation rules in ordinary code where possible; use AI to help interpret ambiguous descriptions.
3. Handling a support request
Consider a sample ticket about an order delay. Ask the system to find the relevant policy, summarize the issue and prepare a reply. Start with draft replies and read-only lookups. Evaluate record selection before allowing updates to customer systems.
When should you use an agent?
Agents can suit tasks where the required steps depend on intermediate findings. If the steps are predictable, a workflow may be easier to control. Anthropic also notes that agentic approaches can increase latency and cost, so complexity should earn its place through better results.
For your own trial, write down the deliverable before selecting the tool: “Explain this error” and “Produce a verified patch” are different requirements.
Limitations you should understand
Generative AI can produce convincing but incorrect information. Connecting it to tools does not eliminate that problem. NIST's Generative AI Risk Management Profile identifies risks including confabulation, data privacy and information security.
For an agent, an incorrect interpretation can affect a later action. Untrusted text in a document or web page can also contain instructions that conflict with your task. Treat retrieved content as information to evaluate, not authority to grant new permissions.
A small test before adopting an AI system
The following is a suggested evaluation plan:
- Choose a bounded task: Use sample files or a test project with a known expected result.
- Define success: Specify what must be correct and what evidence the system should return.
- Limit permissions: Give access only to the files and tools needed for the trial.
- Include difficult cases: Try missing fields, ambiguous instructions and a failed tool request.
- Measure the result: Record completion quality, review time, elapsed time and cost.
- Compare with a simpler option: Keep the approach that meets your requirements with manageable effort.
A helpful scorecard is more informative than a dramatic demonstration. For example, record how often the system selects the right input file and how many corrections a reviewer must make.
Frequently asked questions
Does an agent have to work without human input?
No. Agentic systems can stop for clarification or review. The degree of autonomy depends on the design and permissions.
Is an AI agent always better than a chatbot?
No. Match the system to the work. A clear answer may be all you need for an explanation or first draft.
Can I use agents with C# or SQL Server projects?
A system with suitable integrations could assist with those projects. Start with a test repository or dummy database and evaluate proposed changes before applying them to production.
Final thoughts
Choose AI tools by the work they reliably complete. Start with a specific task, clear success criteria and a small trial. Expand tool access only when the results justify it.
Sources and further reading
- Anthropic: Building effective agents — architectural concepts; originally published in December 2024, with a note that tooling has since evolved.
- NIST: Generative Artificial Intelligence Profile — risks and risk-management considerations.
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