WorkerKitEvaluating enterprise AI automation platforms
Compare enterprise AI automation platforms on one real job, with mandatory controls, acceptance tests, total operating cost and a clear decision record.
By Adam Shamos. Updated .
Evaluate an enterprise AI automation platform by running a bounded job against your current process, checking mandatory constraints first, and comparing the quality, cost and effort of the resulting operation. The deliverable is a decision record your operations, engineering, security and budget owners can all inspect.
A successful demonstration is one piece of that record. You also need to know what happens when inputs are wrong, access is refused, work stops halfway through, and someone needs to recover the result. Start with the broader enterprise AI automation framework if you are still deciding which work belongs in the evaluation.
Write the job and the baseline before selecting a platform
Choose a job with an observable outcome and an owner who can judge it. Keep the input population, application accounts and acceptance criteria consistent across the current system and the candidate. Include the tools and human work already in use; replacing a capable existing process needs a specific gain.
For WorkerKit, Inbound Lead Qualifier gives a concrete starting point. This worker kit creates an AI worker that reads the inquiry and CRM, qualifies the lead, creates or updates a contact, logs a note and prepares a first-reply draft. Its published email permissions allow reads and drafts without sending the reply. It supports on-demand and event-driven runs.
The unit to evaluate is the correctly handled inquiry, with a usable CRM record and draft. A generated paragraph alone does not complete that job. Record how the current process handles the same work, including the time people spend checking matches, correcting records and sending the final reply.
Use a different job if that outcome does not matter to your business. The support triage guide covers another starting point. If the process combines fixed rules and judgement, decide which steps need each before comparing AI agents with RPA.
Separate mandatory constraints from preferences
A required control is a pass-or-reject decision. A preference helps choose between configurations that already pass. Putting both into one weighted score lets a convenient interface compensate for a missing control, which is usually not what the owners intended.
| Mandatory decision | Evidence to collect | Reject or redesign when |
|---|---|---|
| Which data may be processed and where | The actual data path, applicable service terms and the deployment arrangement | The arrangement cannot meet the company's requirement |
| Which app actions may occur | Reviewed grants and observed allowed and refused calls | The job requires broader authority than the company permits |
| Where a person must approve | A demonstrated review path before the specified action | The process can perform that action before the required review |
| What records must be retained | Available fields, retention periods and a demonstrated retrieval process | The necessary evidence cannot be retained or retrieved |
| How the job is stopped and recovered | A bounded stop exercise and a recovery procedure | Nobody can prevent further authorized work or identify completed changes |
| Which service commitments are required | Terms and operational evidence for the intended account and environment | A mandatory commitment remains unsupported |
Write the exact requirement beside each row. "Has governance" is not a test. "A worker granted CRM reads cannot update a record" is. "Has review" is not a test either; "a customer email cannot be sent before the rep approves it" defines an observable boundary.
Fit these requirements into your company's existing risk process. The NIST AI RMF Playbook provides voluntary suggestions organized around governing, mapping, measuring and managing AI risk; NIST explicitly describes it as adaptable guidance rather than a checklist to complete in full. Your evaluation still needs decisions specific to this job.
Give each buyer an explicit decision
Four people can watch the same run and reasonably reach different conclusions. Assign the decision each owns and the evidence they need before running the trial.
| Owner | Evidence they review | Reason to reject the proposed scope |
|---|---|---|
| Operations | Accepted outcomes, exception handling, review minutes and response time | The job misses essential cases or creates an unmanageable review queue |
| Engineering | Setup effort, app behavior, recoverability, change effort and ongoing maintenance | The candidate duplicates existing infrastructure without a useful gain or cannot be maintained by the available team |
| IT and security | Data paths, app grants, refusals, credential handling, records and required review boundaries | A mandatory access or data-handling requirement fails |
| Budget owner | Total operating cost, acceptable output, capacity limits and the cost of ongoing ownership | The proposed operating result cannot justify its cost under the stated assumptions |
For every rejection, record whether the problem belongs to the platform, the chosen configuration or the job itself. A CRM update on the wrong person can be a method failure even when the access control works exactly as configured. Giving the worker broader permissions would not solve that problem.
Run the failure checks as well as the successful job
Use permitted test data and accounts where the effects can be inspected and repaired. Keep real customer sends, purchases and irreversible operations out of failure exercises. Record the configuration, input, expected result, actual result and relevant run identifiers for every check.
| Check | What to do | Evidence the reviewer needs |
|---|---|---|
| Access refusal | Attempt an app operation excluded by the worker's grant using a test record | The operation is refused, the record remains unchanged and the refusal is inspectable |
| Incorrect input | Supply an ambiguous inquiry or a wrong but accessible record identifier | The job's validation or review catches the issue, or the case is recorded as a failed acceptance check |
| Partial write | Arrange a permitted test write followed by a refused or failed later step | The record identifies what already changed, and the recovery procedure handles it without assuming rollback |
| Review | Produce a draft or decision request, then inspect the connected app before a person responds | The specific action reserved for review has not happened; unrelated authorized writes are understood |
| Spending limit | Use a small authorized budget and exercise the configured limit | The refusal or stop is visible and usage can be reconciled to the run's cost record |
| Cadence | Run through the supported event or scheduled path, then compare the source with completed work | Actual start times, missing work, duplicate deliveries and follow-up responsibilities are accounted for |
| Pause and resume | Pause the affected worker, check subsequent requests, then resume under authorization | The observed behavior matches the operating procedure, including the treatment of work already in progress |
| Exit and evidence retrieval | Retrieve the job definition and required records using the available interfaces, then inspect the result | The actual fields, formats, permissions and gaps are known before the team depends on them |
Access refusal and incorrect input test different things. The first asks whether authority is bounded. The second asks whether the method makes a good decision inside that boundary. An Action worker can perform an exact app operation without a model and still receive the wrong input from its caller. Neither a permission check nor a completed run establishes business accuracy.
For the lead qualifier, inspect the saved contact, qualification note and draft separately. If contact creation succeeded before a later failure, retrying the entire job may repeat work. The evaluation should show how the operator identifies that state and decides what to do next. Keep the run receipt alongside the observed app result.
The cadence check also needs a source-side record. A worker cannot report an event it never received. Assign responsibility for reconciling incoming work with completed and unresolved items. State the actual schedule or supported event; persistence between requests does not imply continuous monitoring.
Test the WorkerKit configuration you would actually operate
WorkerKit keeps the job's method, app grants, spending limits and run records with the worker. The useful comparison is whether those operating mechanics improve the proposed job over your current arrangement, including the work your engineers and operators still own.
Review each app grant for the actual connection. WorkerKit holds built-in app credentials and supports field restrictions for built-in apps. Do not assume a custom MCP connection has identical field handling. Test the data visible to the worker and document who maintains the custom service.
A draft boundary is concrete when the worker lacks the send operation. A question to a person is different from a policy that holds every app write for approval. If your requirement is approval before each CRM change, the lead qualifier's authorized CRM writes do not satisfy it merely because the email reply remains a draft. Redesign that job or reject that scope.
Use per-worker pause in the operating procedure. Treat stopping and correcting as separate actions: pausing does not undo changes already made. A requirement to halt many jobs needs a demonstrated procedure with responsibility for every affected worker, rather than an assumed account-wide control.
For retention, export, deployment location or other procurement requirements, inspect the specific service and agreement offered. A record view does not prove that an export includes all required fields, and API access does not prove an existing connection to your evidence system. Complete the retrieval exercise before accepting that requirement as satisfied.
Score preferences only after the required checks pass
Choose weights before viewing results. The following is a proposed worksheet, not a ranking of vendors or a universal procurement standard. Change the weights to reflect the job, while keeping mandatory conditions outside the score.
| Preference | Proposed weight | Evidence for the score |
|---|---|---|
| Useful output and operator effort | 35% | First-review acceptance, final acceptance, review minutes and exceptions |
| Engineering and maintenance effort | 25% | Setup work, changes, reconnection and recovery effort during the trial |
| Total cost of the accepted work | 25% | Execution, platform charges, failed attempts, review, correction and upkeep |
| Adaptability to the company's next need | 15% | A demonstrated model or method change and the effort to preserve its controls |
Set a 0-to-4 scoring rule for each preference with job-specific thresholds: zero means it misses the minimum useful outcome, and four means it meets the target you set. Define the intermediate bands before measuring. A score of four earns the row's full weight, two earns half and zero earns none. Add the earned weights for a total out of 100.
Keep the underlying measurements beside the score. A weighted total is a way to organize a decision, not a way to turn judgement into certainty. Do not give unmeasured categories an average score. Mark them unresolved and finish the evidence collection before choosing.
Use cost per acceptable result for the cost row. That article provides the calculation and worksheet, including corrections, failed attempts and the case where no output is acceptable. Compare the current process and WorkerKit on the same accepted deliverable. Released time is capacity unless actual spending falls.
Assemble the decision packet
Keep one compact packet that another owner can review without attending the demonstration:
- The job, its owner, eligible input population and acceptance criteria.
- The current-process baseline and the exact candidate configuration.
- Mandatory requirements with pass, fail or unresolved status and evidence.
- The successful and failed test cases, app effects and recovery results.
- Quality, review effort and total-cost measurements with assumptions.
- Preference scores, recorded disagreements and the decision each owner made.
- The approved operating scope, review date and conditions that trigger a pause or reevaluation.
Choose among four outcomes: retain the current system, run a narrower trial, approve the bounded job, or reject the candidate for this requirement. Approval of one job does not approve every application, every department or every future instruction change.
WorkerKit earns expansion when the resulting work supports it. Browse the kit directory for a starting method, or build a kit when your team already owns one. Review pricing against the measured usage and required capacity. WorkerKit accounts open by invitation through Get started; once invited, evaluate the smallest useful job before extending it across an AI workforce.