How to Monitor AI Workflows

How to Monitor AI Workflows

Monitoring an AI workflow means checking whether it runs reliably, produces acceptable output, uses tools correctly, stays within cost and privacy limits, and fails visibly when something goes wrong.

A workflow can appear healthy because it completed without a technical error, while still producing a poor classification, unsupported summary, or incorrect record.

Monitoring therefore needs two layers:

• operational monitoring — whether the workflow ran, how long it took, which steps failed, and what tools were used;
• quality monitoring — whether the result was accurate, complete, grounded, useful, and appropriate for the task.

A practical monitoring cycle looks like:

Run
→ Record Activity
→ Validate Output
→ Review Exceptions
→ Measure Trends
→ Improve the Workflow

The goal is not to collect the largest possible volume of logs.

It is to capture enough information to detect important problems, understand their cause, and decide whether the workflow should continue, change, or stop.

Define what a healthy workflow looks like

Begin with the expected behaviour.

A healthy workflow should have a clear definition of:

• successful completion;
• partial completion;
• no-data completion;
• human-review status;
• recoverable error;
• unrecoverable error;
• acceptable runtime;
• acceptable cost;
• required output quality; and
• prohibited outcomes.

For example, a document-extraction workflow may count as successful only when:

• the document was read;
• all required fields were returned;
• numeric fields passed validation;
• source evidence was preserved;
• no unsupported value was added; and
• the approved result reached the intended destination.

Technical completion alone is not enough.

Monitor every workflow run

Record basic run information.

Useful fields include:

• workflow name;
• workflow version;
• run identifier;
• trigger type;
• planned start time;
• actual start time;
• completion time;
• final status;
• input source;
• model and provider;
• tool calls;
• output destination;
• review status; and
• error message where applicable.

A run identifier helps connect all activity from one execution.

It also supports duplicate prevention and safe investigation when a workflow retries or partially completes.

Avoid placing complete sensitive source content inside a general monitoring record when an identifier is sufficient.

Distinguish run status clearly

Use more than Success and Failed.

Useful statuses include:

• Completed;
• Completed with no new data;
• Completed with warning;
• Partial result;
• Human review required;
• Source missing;
• Validation failed;
• Model unavailable;
• Tool failed;
• Duplicate prevented;
• Run conflict detected; and
• Cancelled.

Clear statuses help the owner understand what happened without opening every result.

A partial report should not look identical to a complete report.

A workflow that stopped safely because required information was absent may be behaving correctly.

Capture step-level activity

A final error message may not reveal where the problem began.

Step-level monitoring should show:

• the input received by each step;
• the output it produced;
• the route selected;
• validation results;
• tool parameters;
• tool responses;
• retries;
• warnings; and
• failures.

When the final report contains an incorrect amount, step-level activity can show whether the error occurred during source reading, extraction, calculation, summarisation, or saving.

Focus on the first step that became incorrect.

Later errors may be consequences rather than causes.

Use intermediate outputs

Intermediate output makes long or complex workflows easier to understand.

Useful checkpoints may include:

• prepared input;
• extracted fields;
• selected category;
• validation result;
• calculated metrics;
• retrieved source;
• draft output;
• tool response; and
• final review status.

Do not expose every internal value automatically.

Choose checkpoints that help diagnose known risks.

Intermediate output can also help reviewers understand how the final result was assembled.

It should not reveal unnecessary personal information, credentials, or confidential source content.

Monitor model output quality

Operational success does not prove output quality.

Monitor task-specific quality measures.

For classification, track:

• accuracy by label;
• confusion between labels;
• Other and Unclear rates;
• missed urgent cases;
• false urgent cases; and
• human corrections.

For extraction, track:

• correct fields;
• missing fields;
• invented values;
• invalid formats;
• source-reference accuracy; and
• reviewer correction rate.

For summaries and drafts, track:

• factual faithfulness;
• completeness;
• unsupported claims;
• required-section coverage;
• tone suitability;
• approval rate; and
• edit time.

Use metrics that match the actual task.

Monitor groundedness

A grounded result is supported by the source information provided to the model.

Check whether:

• important claims appear in the source;
• citations or section references exist;
• extracted values match the original;
• the model separated facts from suggestions;
• missing information remained missing; and
• retrieved evidence was relevant.

Groundedness monitoring is especially important for:

• research;
• document summaries;
• customer replies;
• legal or policy material;
• financial extraction;
• recurring reports; and
• public content.

A fluent answer can still be unsupported.

Sample approved output regularly rather than reviewing only failures.

Monitor human corrections

Reviewer changes are valuable monitoring data.

Record common correction types, such as:

• wrong category;
• missing deadline;
• incorrect name;
• unsupported claim;
• unsuitable tone;
• wrong destination;
• missing source;
• invalid total; and
• unnecessary escalation.

Repeated corrections show where the workflow needs improvement.

If reviewers repeatedly add missing owners, improve extraction or source preparation.

If they often reject one category, revise its definition and examples.

A low correction rate does not always prove quality. Reviewers may approve too quickly, so sample accepted results independently.

Monitor latency and runtime

Track how long workflows and individual steps take.

Useful measures include:

• total runtime;
• model response time;
• tool response time;
• file-processing time;
• queue or waiting time;
• human-review delay; and
• retry delay.

Slow performance may come from:

• a large input;
• a local model loading;
• an unsuitable model;
• repeated tool calls;
• an external API;
• a long retry policy;
• overlapping runs; or
• a blocked review step.

Measure the complete process from input to approved result.

A fast model does not create a fast workflow when review and correction take much longer.

Monitor cost and resource use

Track the cost of useful completed work.

Possible measures include:

• model usage;
• tool charges;
• storage;
• local hardware use;
• electricity;
• review time;
• correction time;
• failed-run cost; and
• cost per approved result.

Sudden cost increases may indicate:

• unusually large inputs;
• repeated retries;
• an agent loop;
• unnecessary model calls;
• duplicate runs;
• a more expensive model;
• a tool used too often; or
• a workflow processing irrelevant records.

Set practical thresholds.

Pause or investigate the workflow when usage changes unexpectedly.

Monitor tools and external actions

Tool activity should be visible.

Record:

• tool name;
• action type;
• parameters;
• account or destination;
• start and completion time;
• returned status;
• error details; and
• confirmation at the destination.

A model may state that an action completed when the tool failed or was never called.

Confirm important write actions by checking the actual destination.

Monitor for:

• duplicate records;
• wrong recipients;
• incorrect file paths;
• excessive permissions;
• unexpected tool selection;
• repeated retries;
• partial writes; and
• actions outside the approved workflow purpose.

Monitor retries and duplicate prevention

Retries can recover temporary failures.

They can also create duplicates when the first action completed but the response was lost.

Record:

• retry reason;
• attempt number;
• delay;
• previous result;
• duplicate check;
• final outcome; and
• whether a write already exists.

Use source identifiers, reporting periods, or destination checks to prevent repeated actions.

Monitor duplicate-prevention events.

A growing number may indicate unstable tools, weak acknowledgement handling, or overlapping schedules.

Monitor scheduled workflows

Scheduled workflows require ongoing operational attention.

Track:

• planned runs;
• completed runs;
• missed runs;
• failed runs;
• partial runs;
• conflict warnings;
• average runtime;
• next run;
• paused status;
• required local services;
• review backlog; and
• output destination.

A scheduled workflow may fail because the desktop application is closed, a local model is unavailable, a file is missing, credentials expired, or an external service changed.

Review run history regularly.

Do not assume silence means success.

Monitor local model dependencies

A local AI workflow depends on the local environment.

Monitor:

• whether the model application is running;
• whether the selected model is available;
• loading time;
• memory use;
• storage;
• response time;
• model errors;
• computer sleep or shutdown;
• software updates; and
• compatibility after changes.

A local model can remove cloud-model dependence, but it creates local operational responsibility.

Test the workflow after updating the model application or replacing the model.

Record which local model version produced each important result where practical.

Monitor provider and model changes

Cloud providers may change:

• model availability;
• names;
• limits;
• price;
• latency;
• tool support;
• structured-output behaviour; and
• account requirements.

Monitor provider errors and unexpected output changes.

Re-run the evaluation set after a material model or provider change.

Do not assume that a workflow remains reliable because its prompt and structure did not change.

External dependencies can change the result.

Keep an approved fallback or pause procedure for important workflows.

Monitor data drift and source changes

The workflow input may change over time.

Examples include:

• new document layouts;
• renamed fields;
• new customer terminology;
• different languages;
• longer messages;
• new product categories;
• changed reporting periods;
• revised policies; and
• new source systems.

Monitor:

• unknown labels;
• higher missing-field rates;
• more Other results;
• increased review;
• invalid formats;
• retrieval failures; and
• changes in input size.

These signals may indicate data drift.

Update the schema, taxonomy, examples, source preparation, or workflow route when the real input changes.

Monitor privacy and security

Monitoring itself can create privacy risk.

Logs may contain:

• source text;
• personal information;
• model output;
• tool parameters;
• file paths;
• identifiers;
• error details; and
• external destinations.

Define:

• what is logged;
• who can access it;
• where it is stored;
• how long it is retained;
• which fields are masked;
• how exports are controlled; and
• how records are deleted.

Do not log credentials or secrets.

Monitor unexpected tool access, unusual destinations, denied permissions, and attempts by untrusted input to influence tool use.

Create alert thresholds

Not every unusual event requires immediate interruption.

Define thresholds based on impact.

Possible alerts include:

• any high-impact write failure;
• any unauthorised tool attempt;
• missed scheduled run;
• repeated provider errors;
• cost above the approved limit;
• unusual runtime;
• review backlog above the limit;
• invalid-output rate above the threshold;
• sudden increase in Other or Unclear;
• drop in approval rate; or
• high-impact classification error.

Avoid creating so many alerts that important warnings are ignored.

Group low-risk recurring issues into a periodic review.

Escalate urgent or consequential failures immediately.

Define monitoring ownership

Every important workflow needs an owner.

Define who:

• reviews run history;
• investigates errors;
• approves workflow changes;
• manages models and providers;
• checks tools and credentials;
• reviews cost;
• updates the test set;
• responds to privacy incidents;
• pauses the workflow; and
• decides when it can resume.

Also define review frequency.

A daily customer-support workflow may need daily monitoring.

A monthly internal report may need review after each run.

Monitoring without ownership becomes passive record collection.

Build a monitoring dashboard or report

A simple monitoring view may include:

Area	Example measure
Runs	Completed, partial, failed, missed
Quality	Accuracy, approval, corrections
Operations	Runtime, retries, tool errors
Cost	Cost per approved result
Review	Backlog and average review time
Risk	High-impact errors and access issues
Schedule	Upcoming runs and conflicts

A failure rate increasing from 1% to 8% matters even when the complete number of successful runs remains high.

Monitor workflows in Feluda

Feluda provides several places to inspect workflow behaviour.

In Workbench, the Activity drawer shows tool calls, input data, output data, and error messages.

In RunFlows, the output panel provides visibility into the saved workflow's run and its errors.

In Studio, Emit blocks can expose intermediate results at selected points in the flow.

These features help identify the first step that produced an unexpected value.

Use clear block names such as:

• Validate Input;
• Extract Invoice Fields;
• Check Required Values;
• Draft Summary;
• Save Journal Entry; and
• Return Review Error.

Clear names make activity easier to interpret.

Use the Feluda Journal for monitoring output

Feluda includes a built-in Journal interface and tool.

When enabled and supplied to an AI workflow, it can write Markdown entries during conversations and flow executions.

The Journal can support:

• daily workflow summaries;
• monitoring notes;
• reviewable scheduled output;
• exception reports;
• manual observations; and
• historical results.

Feluda also provides a Journal Monitor for watching a selected journal.

Do not write sensitive data to the Journal automatically without reviewing access and retention.

Use concise status entries and references when complete source content is not required.

Monitor scheduled runs in Feluda

Feluda's Schedule Manager supports once, daily, weekdays, weekly, and monthly schedules in paid plans.

It provides:

• upcoming runs;
• recent history;
• conflict warnings;
• pause controls; and
• resume controls.

Review scheduled history after initial deployment and after any workflow, model, tool, or source change.

The desktop application and required local services must be available at run time.

A missing run may indicate an environment problem rather than a workflow logic problem.

Use Feluda permissions during investigation

Tool errors may be caused by flow permissions.

Feluda flow permissions can control allowed or denied URLs, IP addresses, file paths, and ports.

When a tool fails, inspect:

• the Activity log;
• tool input;
• tool error;
• required destination;
• permission settings; and
• whether the action is appropriate for the workflow.

Do not broaden permissions automatically to remove an error.

Grant only the access required by the approved process.

Retest after changing permissions.

Create a monitoring workflow in Feluda

A practical monitoring pattern may use:

Workflow Result
→ Expression Determine Status
→ LLM Summarise Errors or Exceptions
→ Journal Entry
→ Output

Another pattern may use:

Scheduled Run Data
→ LLM Extract Failures
→ Expression Check Thresholds
→ Output: Review Required

Use:

• Expression for exact status and threshold checks;
• LLM Extract for structured information from written logs;
• LLM for concise exception summaries;
• Emit for intermediate debugging;
• Output for clear monitoring states; and
• the Journal for approved historical entries.

Do not ask an AI model to determine an exact threshold that a fixed expression can check.

Test monitoring before regular use

Deliberately create test events.

Include:

• successful run;
• no-data run;
• missing input;
• invalid output;
• unavailable provider;
• stopped local model;
• tool permission failure;
• incorrect destination;
• retry;
• duplicate prevention;
• overlapping schedule;
• human-review route; and
• privacy-sensitive error.

Confirm that:

• the correct status is recorded;
• the error identifies the failed step;
• sensitive data is not exposed unnecessarily;
• the owner can find the result;
• retry behaviour is visible;
• duplicate actions are prevented;
• review cases are not lost; and
• the workflow can be paused safely.

Monitoring that has not been tested may fail when it is needed most.

Review trends, not only individual failures

Review trends such as:

• rising validation failures;
• slower model responses;
• increasing cost;
• more tool retries;
• lower approval rates;
• growing review backlog;
• more unknown categories;
• repeated missing sources; and
• frequent schedule conflicts.

Compare results before and after workflow changes.

Keep previous versions and evaluation results so improvements and regressions can be identified.

Common monitoring mistakes

Avoid:

• monitoring only technical errors;
• treating completion as proof of quality;
• logging sensitive data unnecessarily;
• collecting logs without an owner;
• failing to monitor tool destinations;
• ignoring human corrections;
• measuring only averages;
• hiding partial and no-data runs;
• allowing alert overload;
• failing to monitor local dependencies;
• changing models without reevaluation; and
• waiting for users to report every problem.

Monitoring should make the workflow more understandable and controllable.

Monitor the outcomes that matter

Begin with one workflow.

Define healthy behaviour, important failures, quality requirements, cost limits, and ownership.

Record each run and selected intermediate steps.

Review both operational and output-quality metrics.

Add alerts for consequential failures and trends.

Use Feluda's Activity log, RunFlows output, Emit blocks, Journal, and Schedule Manager history to inspect and document behaviour.

AI workflow monitoring is most useful when it helps people detect problems early, understand their cause, and improve the process before a small error becomes a repeated automated outcome.