AI and agent templates

All 24 AI and agent templates

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  Agent task brief

  Brief an AI agent like a new colleague: the goal, the context, the limits, when it is done, which tools it may use and when to stop and ask.

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  Agent handoff map

  Who hands what to whom when people and agents share a piece of work, and the check that has to pass at each handoff.

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  Human + agent workshop

  Sort a team’s tasks by how much judgement and how much repetition they involve, to decide what agents do and what people keep.

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  Eval review board

  Go through test cases for an AI feature: the input, what good looks like, what it actually said and the verdict.

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  Agent workflow design

  Design a workflow an AI agent runs: what starts it, what it gathers, what it does, how its work is checked and how it hands back.

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  Agent permissions review

  Decide what an AI agent may do on its own, what needs a person’s approval and what it must never do, action by action.

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  AI pilot plan

  Run a small, honest pilot of an AI tool: the job it should do, the baseline, a test group, what you measure and the decision at the end.

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  Prompt iteration log

  Keep track of how a prompt evolves: each version, what changed, how it did on your test cases and whether to keep it.

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  Human review queue

  A board for people to review what agents produced: waiting, in review, sent back with a reason, and approved.

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  Agent incident review

  Look into a time an AI agent got something wrong: what happened, why it happened, why checks missed it, and what changes.

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  AI use policy draft

  Draft the team’s rules for using AI tools: what’s encouraged, what’s off limits, what data can go in, and when to say AI was used.

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  Model option comparison

  Compare AI model or tool options for one job on quality, cost, speed, privacy and fit, using your own test cases.

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  Automation candidates

  List repetitive tasks and score each for automation: time spent, how rule-based it is, data available and the cost of a mistake.

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  Assistant knowledge sources

  Map what an AI assistant draws on to answer questions: each source, who owns it, how fresh it is and what it must not use.

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  AI feature spec

  Specify an AI-powered feature: the user and job, inputs and outputs, how it can fail, the guardrails and how you’ll measure quality.

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  Red team session

  Spend an hour trying to make an AI feature misbehave: the attempts, what worked, how bad it was and the fix.

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  AI adoption roadmap

  Plan how a team or company adopts AI in stages: learn, try safely, embed in workflows, and scale what works.

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  Golden test set

  Build the set of test cases an AI feature must pass: realistic inputs, the expected answer, tags and why each case matters.

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  Agent run review

  Step through one run of an AI agent: what it did at each step, whether it was right, and what to change in its instructions or tools.

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  Team prompt library

  The prompts your team reuses, with the inputs each one needs, when it works best and who keeps it up to date.

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  AI use case canvas

  Think an AI idea through before building it: the problem, the people, the data, what good looks like, risks and who stays in the loop.

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  AI risk review

  Check an AI feature against the risks that matter before launch: how likely, how bad, what you do about it and who owns it.

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  Model card

  Describe a machine learning model for the people who rely on it: what it does, its data, how well it works, for whom, and its limits.

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  Agent control room

  Watch and steer an AI agent: it posts its plan, status and tasks here; you move cards and comment to redirect it.