Core Use-Case Agent Evaluation
Test AI agents on real work before trusting launch claims
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 94%
Choose a small portfolio of representative tasks that reflects the work an intended user actually performs, rather than accepting benchmark scores or promotional demonstrations. Include research-heavy work, an action-producing task, and at least one workflow where a specialized tool provides a meaningful baseline. Give each system a defined objective, constraints, and output structure, then observe output quality, elapsed time, data access, factual reliability, and the amount of human intervention required. Validate consequential claims and compare the general agent with both the current human workflow and the strongest specialist product. The output is a practical adoption decision: use now, experiment further, route the task to a specialist, or defer until the technology improves.
Origin
Extracted from Marketing Against The Grain, where ChatGPT Agent was tested through three marketer-oriented use cases and compared with GenSpark on presentation creation.
Core principles
- 01Real workflows reveal more than vendor benchmarks
- 02Representative use cases should cover research and action
- 03Completion time matters alongside output quality
- 04Specialized tools provide useful comparison baselines
- 05Human review remains necessary for factual validation
How to run it
- 1
Choose representative work
Select a compact set of recurring use cases that reflects what the target user genuinely needs to accomplish. Cover both information retrieval and action-producing work.
Pro tip Use tasks that already consume measurable employee time.
Watch out Do not select only tasks featured in the vendor's launch demonstration.
- 2
Specify success
Define the requested research, constraints, output format, and expected level of detail before starting each run.
Pro tip Reuse the same substantive brief when comparing tools.
Watch out A vague prompt makes poor output impossible to diagnose fairly.
- 3
Run realistic trials
Execute each task through the actual product experience and let the agent use its available tools. Track interruptions, permissions, failures, and manual takeovers.
Pro tip Run independent trials in parallel when the platform permits it.
Watch out Do not confuse an attractive interface with successful task completion.
- 4
Measure time and quality
Record elapsed time, completeness, usefulness, and how much correction the result needs. Compare these results with the existing human process.
Pro tip Separate research quality from the quality of the final artifact.
Watch out A task that eventually completes may still be slower than a specialist or human.
- 5
Compare a specialist
Run an equivalent task through a mature specialized product when one exists. Assess whether the general agent offers enough flexibility or integration value to offset weaker performance.
Pro tip Compare usable final outputs, not just feature lists.
Watch out Avoid declaring a general winner from one category of task.
- 6
Validate and route
Verify important facts, identify the workflows where the agent is already useful, and route weaker workflows elsewhere. Revisit the decision as the products improve.
Pro tip Maintain a task-to-tool routing table.
Watch out Do not automate unverified output directly into consequential workflows.
In the wild
A growth team tests a new agent on competitor research, ICP construction, and presentation creation. It records output quality, elapsed time, factual errors, and required intervention, then compares the presentation task with a specialist slide generator.
→ The team adopts the general agent for research, keeps the specialist for decks, and schedules another evaluation after future releases.
Common mistakes
Trusting launch benchmarks
Academic and vendor benchmarks may show capability without proving that the product saves time on the team's real workflows.
Judging only the interface
A polished virtual-computer experience can look impressive even when task execution is slow or incomplete.
Ignoring specialized competitors
Evaluating a general agent in isolation conceals categories where a focused tool already produces better results much faster.
Is it for you?
Best for
It is best for marketers and operators evaluating whether a new AI agent can improve recurring knowledge-work workflows.
Not ideal for
It is not ideal for evaluating high-risk production automation without additional security, reliability, and compliance testing.
From the transcript
“Well, we're going to put it through its paces with three core use cases that any marketer or growth operator or someone who's looking to…”
“So we can actually give you the unbiased version. Should you use this or not?”
“So for 45 minutes, I don't think you're saving that much time compared to Gen Spark, which was like less than five minutes.”
From the episode
The New ChatGPT Agent Promised to Save Me Hours - Did It?