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Agent use case evaluation

Agent use case evaluation

Agent use case evaluation is a decision framework for identifying work that current-generation AI agents can handle reliably. The framework addresses a common pitfall: organizations either attempt automation of tasks too complex for reliable execution, or dismiss agent potential entirely due to past failures with poorly chosen use cases.

The sweet spot for agent automation sits at the intersection of low cognitive load and high repetition. These tasks prove too dynamic for traditional scripts but remain predictable enough for AI to handle consistently. They appear across businesses in data entry, research, qualification, and triage work — the operational layer where automation saves time while maintaining quality consistency.

The five-point filter

A practical methodology for agent evaluation emerged from Vercel's production deployments. Five criteria must all be satisfied for immediate agent implementation.

The work must be verifiable. If checking whether the agent performed correctly requires significant effort or expertise, the use case isn't suitable for current-generation agents. Verification should be straightforward, not heroic.

The workflow must be step-wise and bounded. Think "five clicks and completion," not "develop a product strategy." Complex, open-ended processes with many decision branches overwhelm current agent capabilities.

The task must be recurring and painful. People should describe this work as toil or suffering, not as valued intellectual contribution. If the work provides meaning or requires creative judgment, automation removes value rather than adding it.

Inputs and outputs must be known. Understanding exactly what enters the system and what constitutes good output enables both agent design and verification. Unknown or highly variable inputs create unpredictable failure modes.

A human must remain in the loop. The agent prepares work; a person approves, edits, or rejects it. Full autonomy without human oversight isn't appropriate for most business contexts with current technology.

This filter points toward specific applications: back-office flows, operational support, the tedious work nobody chose to perform. These represent accessible value rather than aspirational capability.

Three-question validation

For rapid assessment of any potential agent use case, three questions provide clarity.

Is this work verifiable and bounded? Difficulty checking results suggests the task isn't a first-wave agent application. Verification complexity indicates the judgment required exceeds what agents handle reliably.

Is this work recurring toil? If people don't actively dislike the work, or if it occurs infrequently, an agent likely isn't the right solution. The economics of agent design favor repetitive operations where development investment amortizes across many executions.

Can you articulate the orchestration guardrails? If you cannot answer what tools the agent needs, what data it accesses, what budget constraints apply, and what escalation paths exist, deployment isn't ready. These questions must have concrete answers before building begins.

Three affirmative answers place the use case in the "build it now" category: implement with human oversight while developing organizational capability. Any negative answer suggests waiting for more capable systems or comprehensive agent architecture, rather than pretending current technology can safely automate the work.

Common misconceptions

Organizations often overestimate what agents can handle or underestimate what they should automate. High-profile demonstrations of agent capability on impressive tasks create unrealistic expectations. Production agents succeeding at mundane operations attract less attention than experimental agents attempting complex reasoning.

The gap between demonstration and production remains significant. A chatbot discussing strategy in a controlled demo differs fundamentally from an agent reliably categorizing thousands of support tickets daily. The latter requires the evaluation criteria above; the former merely requires a compelling prompt and tolerance for occasional failure.

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