Jon CursiYou are a COO comparing three managed AI agent proposals. Each vendor gave you a polished demo. One agent summarized a report. Another drafted an email. A third answered questions about a spreadsheet.
Now the proposals are sitting on your desk, and the demos have given you almost nothing useful for choosing between them.
This is the problem with most managed AI agent evaluations. A controlled demo can prove that an agent produces an impressive answer once. Your business needs dependable work across a recurring queue filled with missing context, unusual cases, and shifting priorities.
A better evaluation uses a paid work sample on one real queue. The vendor should also provide an evidence packet that your team can inspect before making a longer commitment.
Demo limits
A vendor chooses the input, conditions, and expected output for a demo. The agent handles one item while everyone watches.
That format avoids the questions that determine whether a deployment will be useful. Buyers need to see how the agent handles the 40th item, a malformed request, or conflicting source material. They need to know how errors are found and corrected. They also need evidence that output stays useful after the novelty wears off.
The first answer is the easy part. The harder test is whether the agent can work through a messy stream of real tasks while staying inside clear boundaries.
Use the demo to understand the idea. Use real work to evaluate the service.
Real work queue
Choose one queue from your operation. It might be a weekly report cycle, a backlog of engineering issues, recurring website work, or administrative requests that follow a familiar pattern.
The queue should recur often enough to show consistency. It should contain the same imperfect inputs your team sees every day. It also needs a definition of done that your team already understands, which lets you grade the work against your standard.
Avoid a sanitized test built for the evaluation. Cleaning up the source material hides the conditions the agent will face after launch.
TaskAdmin's Internal AI service can work across engineering, websites, content, reports, analysis, administrative work, and recurring operations. The best evaluation lane is the one where finished output would matter now and reviewers can tell good work from bad work quickly.
Paid work sample
The work sample should be paid because the vendor is doing real work and the buyer needs a serious review process.
Free trials often become casual experiments. A few people skim the output, somebody says it looks promising, and the team never completes a proper review. A paid sample gives the evaluation an owner, a budget, and an expected decision.
Keep the scope narrow enough to inspect. Define the queue, sample window, expected volume, review owner, and acceptance criteria before work begins. Agree on the evidence the vendor will return when the sample ends.
The sample is useful only when your team can trace what happened. A folder full of selected wins will not tell you whether the service can handle ordinary work.
Evidence packet
The evidence packet is the reviewable record of the sample. It should connect inputs, outputs, corrections, and escalations so your team can audit the work.
Reviewable artifacts
Every completed item should point back to its source and acceptance criteria. Engineering artifacts might include pull requests, tests, and resolved issues. A reporting workflow might produce finished documents with links to the approved sources. An administrative queue might show completed tasks and the context used for each one.
The NextraData case study shows the level of evidence buyers should expect. In the first month, the Internal AI software engineer produced 69 merged pull requests and resolved 42 issues. It touched more than 278,000 lines of code, removed a net 59,000 lines, and authored 57 percent of the team's merged pull requests. The work also modernized testing to 100 percent component coverage and built self-QA workflows for visual verification.
Those figures describe artifacts inside a real engineering workflow. Your evaluation should produce evidence that is equally easy to check in your own systems.
Correction history
Ask for the items that needed revision and what changed afterward. Corrections show whether feedback improves the next round of work.
Review the original output, the problem a person found, and the revised artifact. Then look for another item with the same issue. Repeated mistakes reveal a weak improvement loop even when the final examples look clean.
Escalation record
Useful internal agents recognize when a task needs human judgment or missing context makes the result uncertain.
Inspect the cases the agent routed for review. The record should make the reason clear and identify what information or decision was needed. Silent stalls and unjustified certainty both create operational risk.
Output scorecard
Set the scorecard before the sample starts. Choose measures tied to the queue, such as accepted items, revision rate, turnaround time, or unresolved exceptions. Use only measures your team can verify from the artifacts.
Activity counts can look impressive while the queue stays stuck. The scorecard should show whether useful work reached your team's definition of done.
Buyer review
Start with the corrections and escalations when the evidence packet arrives. Trace a few items through the full loop from source to final output. Then select several completed artifacts at random and have the normal reviewer grade them without vendor guidance.
This review exposes the operation behind the agent. A managed service depends on somebody defining the work, monitoring quality, and improving the deployment as the business changes. TaskAdmin builds and trains each deployment around the client's workflow, then monitors and improves it over time. The How It Works page explains that service model.
Your reviewers should leave the sample knowing which work the agent handled well, where it needed help, and whether the improvement process held up.
Commitment decision
A longer commitment should follow evidence from your work. TaskAdmin's Internal AI service costs $2,500 to $4,000 for setup and $2,500 to $5,000 per month. The initial term is three months, followed by month-to-month service.
Before signing, make sure the proposed first workstream has clear inputs, reviewable outputs, a human owner, and a scorecard tied to finished work. Those details give both sides a useful starting point for the engagement.
If you are comparing managed AI agents, book a live demo and bring one recurring queue. We can map the work, the review points, and the evidence your team should expect.