Review AI Activity and Results
Feluda helps you see both the answer an AI model produced and the actions it took while completing a task.
Reviewing this information helps you understand:
- what the model did;
- which tools were used;
- what information was returned;
- whether an error occurred; and
- whether the final result is suitable to use.
AI-generated results should not be accepted automatically. A response may be incomplete, incorrect, or more confident than the available information allows.
What you should review
A complete review can include several parts.
| Part | What to check |
|---|---|
| AI response | Whether the answer follows your instruction and matches the source |
| Tool activity | Which tools were called and whether they completed |
| Tool result | What information or confirmation the tool returned |
| Error messages | What failed and where the problem occurred |
| Final destination | Whether a file, Journal entry, or connected service contains the expected result |
Not every conversation uses tools. When no tool is used, focus on the AI response and the source information.
Review the AI response first
Begin with the answer shown in the conversation.
Check whether it:
- completed the task you requested;
- used the correct source information;
- followed the requested format;
- respected any length or tone limits;
- handled missing information clearly;
- left out important details; and
- added claims that were not supported by the source.
A polished answer can still be wrong. Compare important details with the original information.
Compare the answer with your instruction
Read your instruction again.
Ask:
- Did the model answer the actual request?
- Did it include every required section?
- Did it use the correct audience and tone?
- Did it follow the requested order?
- Did it avoid anything you told it not to do?
When the answer is incomplete, improve the instruction or ask a focused follow-up question.
For example:
Add the missing deadlines.
Use only dates that appear in the source.
Write "Not provided" when no deadline is available.
Check facts against the source
When the task is based on notes, a file, a message, or another source, compare the answer with that source.
Look for:
- changed names;
- incorrect dates;
- missing amounts;
- invented deadlines;
- statements presented more strongly than the source supports; and
- suggestions presented as confirmed facts.
Ask the model to separate source facts from its own suggestions when both are needed.
For example:
Create two sections:
1. Facts stated in the source
2. Suggested next steps
Do not present suggestions as confirmed facts.
Open the Activity log
When the AI uses a tool in Workbench, open the Activity log.
The Activity log shows the actions recorded during the conversation.
Depending on the task, you may be able to review:
- the tool that was called;
- the information passed to the tool;
- the result returned by the tool;
- the order of multiple tool calls; and
- any error message.
The Activity log helps you confirm what happened instead of relying only on the model's written explanation.
Review the tool name
Confirm that the model used the correct tool.
A tool may have a similar name to another tool but perform a different action.
Check that:
- the selected tool matches the task;
- it belongs to the expected Gene or connection;
- it was intended to read, write, retrieve, or change information; and
- no unnecessary tool was called.
If the wrong tool was used, disable unrelated tools and repeat the request with a more direct instruction.
Review the information sent to the tool
The Activity log may show the parameters or input used for a tool call.
Check whether the model passed:
- the correct title or name;
- the right source text;
- the expected search terms;
- the correct file or location;
- the intended recipient or destination; and
- only the information needed for the task.
A tool can work correctly and still produce the wrong result when it receives incorrect input.
Review the tool result
A tool result may contain:
- retrieved information;
- a success confirmation;
- a created item;
- a returned file path;
- an identifier;
- a warning; or
- an error.
Read the complete result when available.
Do not assume that a success message means the content itself is correct. It may only confirm that the requested action completed.
For example, a Journal tool may successfully create an entry even when the entry contains missing or incorrect information.
Confirm the result at its destination
When a tool creates or changes something, check the destination.
For example:
- open the Journal and read the new entry;
- open the expected file;
- check the connected service;
- review the sent or saved content;
- confirm that the correct record was changed; or
- compare retrieved data with the original source.
This is especially important for write actions.
The Activity log shows what Feluda recorded. The destination shows the final outcome.
Review multiple tool calls in order
Some tasks use more than one tool.
Review the calls in the order they happened.
Ask:
- What information did the first tool return?
- How did the model use that information?
- What was passed to the next tool?
- Did an early mistake affect the later steps?
- Did every tool complete?
A final answer may look incorrect because an earlier tool returned incomplete information.
Finding the first incorrect step makes troubleshooting easier.
Understand common activity states
Tool activity may show different outcomes.
| Outcome | What it usually means |
|---|---|
| Completed | The tool returned a result without reporting an error |
| Failed | The tool could not complete the requested action |
| Denied | The request was outside the tool's allowed access |
| Incomplete | Required information may have been missing |
| No tool call | The model answered directly or did not decide to use the enabled tool |
The exact wording can vary by tool.
Read the returned message for the most useful explanation.
Review error messages
An error message often explains which part of the task failed.
Common causes include:
- a missing setting;
- an unavailable provider;
- an incorrect or expired access key;
- a connection problem;
- missing required information;
- an unsupported action;
- a permission restriction;
- an unavailable file or source; or
- a model that does not support the requested feature.
Correct one issue at a time.
Then repeat the task with a small, simple example.
Avoid repeating write actions too quickly
When a write action reports an error or timeout, confirm whether the action completed before trying again.
Repeating it immediately could create:
- duplicate Journal entries;
- duplicate files;
- repeated messages;
- multiple updates; or
- another unintended result.
Check the destination and Activity log first.
Only repeat the action after you are confident that the earlier attempt did not complete.
Review results from external sources
Some tools retrieve current information from websites, databases, or other services.
Check:
- whether the result came from the expected source;
- when the information was last updated;
- whether important source details were included;
- whether the result is complete;
- whether another source should be checked; and
- whether the AI summary matches the returned information.
Separate the tool's returned information from the model's interpretation when accuracy matters.
Review local model results
A local model can produce useful results, but its quality depends on the selected model and task.
Check whether the model:
- understood the instruction;
- followed the required format;
- handled long source material correctly;
- used tool results accurately; and
- added unsupported information.
Smaller local models may need shorter instructions, smaller source sections, or more structured output requirements.
Compare another model when the result remains unreliable.
Review attachments carefully
When the model uses an attached image, audio file, or document, compare the response with the original attachment.
Check:
- names and numbers;
- dates and times;
- labels and headings;
- missing sections;
- unclear audio;
- unreadable image details; and
- any part the model may have misunderstood.
Ask the model to identify uncertain or unreadable content instead of guessing.
Review workflow results in RunFlows
Workflows can also show output and errors while they run.
In RunFlows, review:
- whether the flow started;
- which step produced the expected result;
- where an error occurred;
- any intermediate output shown by the flow;
- the final output; and
- whether an external action completed.
When a flow fails, begin with the first step that did not produce the expected result.
Do not focus only on the final error. The original problem may have occurred earlier.
Use intermediate output for troubleshooting
Some workflows can show intermediate results.
These results help you see how information changes from one step to the next.
Review whether:
- the input reached the correct step;
- the AI returned the expected structure;
- important information was lost;
- a condition followed the correct path; and
- the next step received usable information.
Intermediate output is especially useful when the final answer is wrong but the workflow still completes.
Decide whether the result is ready to use
A result is ready only when it meets the needs of the task.
Use a simple review checklist:
- Is it accurate?
- Is it complete?
- Is it supported by the source?
- Is the format correct?
- Did every required action complete?
- Is the result appropriate for the audience?
- Does a person need to approve it?
If any answer is unclear, keep the result in review.
Keep a human in the loop
Human review is especially important when a result affects:
- customers;
- employees;
- money;
- contracts;
- legal rights;
- health;
- safety;
- security;
- access to services; or
- other important decisions.
AI can support the work, but it should not be treated as the final authority in high-impact situations.
Use a draft-and-review process before any important write action.
Record useful findings
When a review reveals a repeated problem, record it.
For example:
- the model often misses deadlines;
- a tool needs a particular date format;
- missing fields should be marked as "Not provided";
- a smaller source section produces better results; or
- a particular model follows tables more reliably.
Use these findings to improve the instruction or workflow.
The goal is not only to fix one result. It is to make future results easier to review.
Improve the instruction after review
Turn review findings into clearer rules.
For example, if the model invents missing information, add:
Use only the provided source.
If information is missing, write "Not provided."
Do not guess.
If the output is inconsistent, add:
Return the result using these headings:
Summary
Decisions
Actions
Open Questions
Test the revised instruction with several examples.
Review before moving to Studio
A Workbench task may be ready for a workflow when:
- the instruction produces a useful result consistently;
- the required tools are known;
- the changing input is easy to identify;
- errors are understandable;
- the result can be checked; and
- important actions still include a review step.
A task that cannot be reviewed clearly is not ready for unattended automation.
A practical review routine
Use this routine after an important Workbench task:
- Read the final AI response.
- Compare it with the instruction.
- Check important facts against the source.
- Open the Activity log.
- Review each tool call and result.
- Confirm write actions at their destination.
- Read any errors or warnings.
- Decide whether the result needs correction or approval.
- Improve the instruction when the same issue may happen again.
This process gives you a clear view of both the answer and the actions that produced it.