Use Cases for AI Agents Best Practices

Ten practices for scoping a first agent use case without drowning in frameworks, vertical hype, or unbounded tools.

Use this list at intake, after demos, and before anyone requests production write credentials.

How to Use This Checklist

Work A → D; earlier filters are cheaper.
Check items that are true for the candidate use case.
If A or D fails, prefer a script, workflow, or copilot.
Keep the filled list attached to the one-page use-case brief.
Re-run when tools, risk, or success metrics change.

A - Pick the Right Problem

1. Write a verifiable goal and done_when before naming models. "PR comments posted and CI still green" beats "AI coding helper."
2. Choose task shape before industry packaging. Label research, execution, monitoring, or an explicit hybrid so tools and evals match reality.
3. Prefer problems with existing tools and verification loops. Git+CI, ticket APIs, and searchable docs beat greenfield integrations for a first ship.
4. Reject or rewrite goals that only seniors can score. If every outcome needs an expert trial, ship a copilot or invest in rubrics first.

B - Scope Autonomy and Actions

5. Start at the lowest autonomy that still completes the job. Single-turn tools and supervised goals before long-running exception-only modes.
6. Keep the first tool allowlist tiny and well described. Three sharp tools outperform fifteen vague ones for selection quality and security.
7. Separate read paths from write paths by policy. Prove value read-only or draft-only; gate irreversible actions with humans, caps, and dry-runs.

C - Prove Fit with Evidence

8. Build a golden task set before scaling users. Aim for enough tasks to fail honestly (often 20+), with expected outcomes and cost notes.
9. Require traces, stop conditions, and an escalation owner in the pilot plan. Max turns, budgets, and a named human path are scoping requirements, not polish.

D - Stay Honest About Alternatives

10. Explicitly compare agent vs workflow vs copilot and pick the cheapest win. If the path is fixed, automate without an agent loop; if judgment is subjective, keep humans in the accept path.

Applying the Habits in Order

Stage	Habits	Exit criterion
Intake	1-4	Clear goal, shape, tools/verification reality
Design	5-7	Autonomy level + allowlist + write policy
Pilot	8-9	Golden tasks, traces, stops, owner
Decision	10	Documented alternative comparison

Quick "Do Not Start Here" List

Unattended money movement, identity changes, or clinical decisions.
"Replace the team" as the success metric.
No API, no dry-run, no staging.
Sub-second UX forced onto multi-turn loops.
Framework selection meeting scheduled before problem fit.

First-Use-Case Patterns That Usually Work

Pattern	Why it is realistic	First bounds
PR review / test repair	Strong CI verification	Human merges
Internal doc Q&A with citations	Read-heavy, high volume	No send tools
Ticket triage drafts	Existing queues	Human assigns/sends
Meeting notes → task drafts	Clear artifact	User edits first
Read-only research brief	Finite deliverable	Source requirements

FAQs

How many checklist items must pass for a GO?

Treat 1, 7, 8, 9, and 10 as mandatory for any pilot that might touch real users or systems. Soften others only with written risk acceptance.

Is ten practices enough for enterprise programs?

Yes for first-use-case scoping. Layer industry compliance checklists after these habits, not instead of them.

What if leadership already bought a framework?

Keep the framework as an implementation detail. Still run this list; a framework does not create a good use case.

Can personal productivity agents skip golden tasks?

You can spike casually, but any agent with mail, calendar, or shell access deserves a personal checklist of risky actions and accept/reject tracking.

How small is a "tiny" tool allowlist?

Often 2-5 tools for v1. Add tools only when golden tasks prove a missing capability, not when demos look cooler.

Where should this checklist live?

In the use-case brief or ADR beside goal, shape, tools, autonomy, eval size, and escalation owner.

How do these practices relate to premature-use-case signals?

This list is the positive form. The signals cheatsheet is the red-flag form. Use both in reviews.

Should we optimize for industry verticals in v1?

Usually no. Win on a shape with real tools, then package industry constraints and language once the loop works.

What is a healthy pilot duration?

Long enough to run the golden set repeatedly and observe cost tails - often weeks, not a single demo day - before write expansion.

Do multi-agent designs need a different checklist?

Apply these habits per agent and to the orchestrator. Multi-agent multiplies cost and failure modes; it does not relax verification or ownership.

What is the most common broken first use case?

A chatbot over docs marketed as an agent, with one brittle integration, no traces, and success defined as executive delight.

When is an agent clearly the right first bet?

Branching multi-step work, stable tools, automatic or cheap verification, acceptable latency, and a team willing to operate budgets and escalations.

Use Cases for AI Agents Basics - practice matching and scoring
A Decision Framework: Is This Problem a Good Fit for an Agent? - full ranked decisions
Signals a Use Case Is Premature for an Agent Today - red-flag companion
Task Shape Matters More Than Industry When Picking a Use Case - habit 2 depth
Surveying 2026's Most Common Production Agent Deployments - patterns that already ship

Stack versions: Pins from the category manifest (verify at build): OpenRouter (~315+ models, July 2026 pricing/fees); LangGraph 1.0+; CrewAI 1.14+; Microsoft Agent Framework 1.0; Vercel AI SDK 6; Pydantic AI (latest); LlamaIndex (latest); OpenAI Agents SDK (latest + MCP); MCP (Linux Foundation governance); A2A (HTTP+SSE+JSON-RPC 2.0); Solana @solana/web3.js + @solana/spl-token.

Use Cases for AI Agents Best Practices

Ten practices for scoping a first agent use case without drowning in frameworks, vertical hype, or unbounded tools.

Use this list at intake, after demos, and before anyone requests production write credentials.

How to Use This Checklist

Work A → D; earlier filters are cheaper.
Check items that are true for the candidate use case.
If A or D fails, prefer a script, workflow, or copilot.
Keep the filled list attached to the one-page use-case brief.
Re-run when tools, risk, or success metrics change.

A - Pick the Right Problem

1. Write a verifiable goal and done_when before naming models. "PR comments posted and CI still green" beats "AI coding helper."
2. Choose task shape before industry packaging. Label research, execution, monitoring, or an explicit hybrid so tools and evals match reality.
3. Prefer problems with existing tools and verification loops. Git+CI, ticket APIs, and searchable docs beat greenfield integrations for a first ship.
4. Reject or rewrite goals that only seniors can score. If every outcome needs an expert trial, ship a copilot or invest in rubrics first.

B - Scope Autonomy and Actions

5. Start at the lowest autonomy that still completes the job. Single-turn tools and supervised goals before long-running exception-only modes.
6. Keep the first tool allowlist tiny and well described. Three sharp tools outperform fifteen vague ones for selection quality and security.
7. Separate read paths from write paths by policy. Prove value read-only or draft-only; gate irreversible actions with humans, caps, and dry-runs.

C - Prove Fit with Evidence

8. Build a golden task set before scaling users. Aim for enough tasks to fail honestly (often 20+), with expected outcomes and cost notes.
9. Require traces, stop conditions, and an escalation owner in the pilot plan. Max turns, budgets, and a named human path are scoping requirements, not polish.

D - Stay Honest About Alternatives

10. Explicitly compare agent vs workflow vs copilot and pick the cheapest win. If the path is fixed, automate without an agent loop; if judgment is subjective, keep humans in the accept path.

Applying the Habits in Order

Stage	Habits	Exit criterion
Intake	1-4	Clear goal, shape, tools/verification reality
Design	5-7	Autonomy level + allowlist + write policy
Pilot	8-9	Golden tasks, traces, stops, owner
Decision	10	Documented alternative comparison

Quick "Do Not Start Here" List

Unattended money movement, identity changes, or clinical decisions.
"Replace the team" as the success metric.
No API, no dry-run, no staging.
Sub-second UX forced onto multi-turn loops.
Framework selection meeting scheduled before problem fit.

First-Use-Case Patterns That Usually Work

Pattern	Why it is realistic	First bounds
PR review / test repair	Strong CI verification	Human merges
Internal doc Q&A with citations	Read-heavy, high volume	No send tools
Ticket triage drafts	Existing queues	Human assigns/sends
Meeting notes → task drafts	Clear artifact	User edits first
Read-only research brief	Finite deliverable	Source requirements

FAQs

How many checklist items must pass for a GO?

Treat 1, 7, 8, 9, and 10 as mandatory for any pilot that might touch real users or systems. Soften others only with written risk acceptance.

Is ten practices enough for enterprise programs?

Yes for first-use-case scoping. Layer industry compliance checklists after these habits, not instead of them.

What if leadership already bought a framework?

Keep the framework as an implementation detail. Still run this list; a framework does not create a good use case.

Can personal productivity agents skip golden tasks?

You can spike casually, but any agent with mail, calendar, or shell access deserves a personal checklist of risky actions and accept/reject tracking.

How small is a "tiny" tool allowlist?

Often 2-5 tools for v1. Add tools only when golden tasks prove a missing capability, not when demos look cooler.

Where should this checklist live?

In the use-case brief or ADR beside goal, shape, tools, autonomy, eval size, and escalation owner.

How do these practices relate to premature-use-case signals?

This list is the positive form. The signals cheatsheet is the red-flag form. Use both in reviews.

Should we optimize for industry verticals in v1?

Usually no. Win on a shape with real tools, then package industry constraints and language once the loop works.

What is a healthy pilot duration?

Long enough to run the golden set repeatedly and observe cost tails - often weeks, not a single demo day - before write expansion.

Do multi-agent designs need a different checklist?

Apply these habits per agent and to the orchestrator. Multi-agent multiplies cost and failure modes; it does not relax verification or ownership.

What is the most common broken first use case?

A chatbot over docs marketed as an agent, with one brittle integration, no traces, and success defined as executive delight.

When is an agent clearly the right first bet?

Branching multi-step work, stable tools, automatic or cheap verification, acceptable latency, and a team willing to operate budgets and escalations.

Use Cases for AI Agents Basics - practice matching and scoring
A Decision Framework: Is This Problem a Good Fit for an Agent? - full ranked decisions
Signals a Use Case Is Premature for an Agent Today - red-flag companion
Task Shape Matters More Than Industry When Picking a Use Case - habit 2 depth
Surveying 2026's Most Common Production Agent Deployments - patterns that already ship

Stack versions: Pins from the category manifest (verify at build): OpenRouter (~315+ models, July 2026 pricing/fees); LangGraph 1.0+; CrewAI 1.14+; Microsoft Agent Framework 1.0; Vercel AI SDK 6; Pydantic AI (latest); LlamaIndex (latest); OpenAI Agents SDK (latest + MCP); MCP (Linux Foundation governance); A2A (HTTP+SSE+JSON-RPC 2.0); Solana @solana/web3.js + @solana/spl-token.

Use Cases for AI Agents Best Practices

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Use Cases for AI Agents Best Practices