Resource
Blog Post Template
A reusable long-form content template with variables for topic, audience, tone, target keyword, content angle, outline, and call to action.
Agent Task Template
A reusable AI agent task template with variables for objective, context, available tools, constraints, success criteria, failure handling, and output format.
Overview
Vague agent task prompts produce either excessive confirmation-seeking or unconstrained execution — neither of which is useful in a real workflow. This template encodes everything an agent needs to act autonomously: the objective stated as an outcome, the tools it can use, explicit boundaries on what it should not do, and measurable success criteria so it can self-assess before completing the task. The failure handling variable prevents the agent from silently delivering incomplete results when it hits a blocker.
How to use this resource
-
Define objective as an outcome, not a process
The objective variable should describe what done looks like, not what to do. 'Produce a structured comparison of three vendor proposals with a recommendation' is an outcome. 'Research vendors' is a process.
-
Fill in the template variables
Open in Prompt Template Builder. Variables: agentRole, objective, context, availableTools, constraints, successCriteria, failureHandling, outputFormat.
-
Write testable success criteria
Success criteria should be verifiable by the agent without asking you. 'The output includes a recommendation with reasoning' is testable. 'The output is good' is not.
-
Specify failure handling explicitly
The failureHandling variable prevents silent failure. Tell the agent what to do when it hits a blocker: 'If a source is unavailable, note it and continue with remaining sources' is more useful than no instruction.
Why This Works
- Requiring the agent to confirm its understanding of the objective before executing surfaces misinterpretations before they result in wasted work
- Explicit constraints prevent well-intentioned agents from taking actions that are outside the intended scope — the most common source of agentic failures
- Testable success criteria allow the agent to self-assess rather than relying on the human to determine if the task is complete
- Failure handling instructions eliminate the silent failure mode where the agent delivers a partial result without flagging what is missing
Best for
- Multi-step tasks where the agent needs to make decisions without constant human input
- Workflows where you want explicit confirmation of understanding before execution starts
- Tasks with clear success criteria that the agent can evaluate its own output against
- Recurring agent tasks that benefit from a stable, reusable prompt structure
Not for
- Simple single-turn requests — the structure overhead isn't justified for quick lookups
- Tasks requiring real-time tool access you haven't configured in the agent environment
- Situations where the constraints or success criteria cannot be specified in advance
Use cases
- Dispatching an agent to research, summarize, and structure content from multiple sources
- Running an automated review or audit task with explicit pass/fail criteria
- Building a reusable task template for a recurring agent workflow in your team
- Testing agent behavior with consistent task prompts across different models
FAQ
How do I fill the success criteria variable in an agent task template?
Write {{successCriteria}} as something the agent can verify without asking you — "the output includes a recommendation with reasoning" is testable, "the output is good" is not. The template ends with "Report results against the success criteria when done," so measurable criteria let the agent self-assess. You fill the variables in the Prompt Template Builder, then run the assembled prompt in your own agent.
What does the failure handling variable prevent in an agent prompt?
The {{failureHandling}} variable prevents silent failure — the mode where an agent delivers a partial result without flagging what's missing. Filling it with an instruction like "If a source is unavailable, note it and continue with remaining sources" tells the agent what to do at a blocker. The template also makes it confirm the objective and state a plan before executing.
When is this agent task template overkill?
For simple single-turn requests — notFor says the structure overhead of eight variables plus the confirm-plan-execute preamble isn't justified for quick lookups. It also assumes tools you've actually configured in the agent environment; listing tools in {{availableTools}} that aren't wired up won't grant the agent real access.
More resources from Prompt Template Builder
Resource
Pull Request Review Template
A reusable PR review prompt template with variables for repository, branch, change summary, changed files, review criteria, and risk areas.
Resource
Bug Investigation Template
A reusable bug investigation template with variables for symptom, environment, reproduction steps, expected and actual behavior, error logs, and affected version.
Resources that pair well
Resource
Bug Triage Assistant
Convert scattered bug notes, Slack messages, or user complaints into structured engineering tasks with reproduction steps, severity, and root cause hypothesis.
Resource
API Request Variable Builder
Turns a reusable API request prompt into a clean variable set covering endpoint, method, auth, payload, response format, and error handling.
Related tools
Tool
Prompt Template Builder
Build reusable prompt templates with {{variable}} placeholders, live preview, and export.
Workflows that use this resource
Workflow
AI Reusable Prompt Template Workflow
Turn a prompt that worked once into one you can reuse — pull the winning prompt out of the chat, mark the parts that change as variables, and lock it into a clean template.
Guides for this resource
Guide
How to Turn a Good Prompt Into a Reusable Template
You have a prompt that works, but you rewrite it by hand every time something changes — and sometimes you miss a spot or break a rule that mattered. Here's how to turn it into a template with variables for what changes and fixed rules for what shouldn't.
Guide
How to Make AI Follow Acceptance Criteria
The output reads well and answers the question, so you almost ship it — then you notice it broke a rule you cared about and skipped a section you needed. "Looks good" was never the test. Here's how to give AI the acceptance criteria up front and check the output against them before you rely on it.
Guide
How to Prepare Context Before Starting a New AI Chat
Open a fresh chat, type "continue where we left off," and the model just guesses — it knows nothing about the project, the decisions, or the goal. Here's how to assemble a short context packet first, so a new chat starts aimed instead of blind.
Guide
Give an AI Agent Clear Stop Conditions
You wrote what the agent should do, but not when to stop — so it keeps going, retries a failing tool, guesses a missing field, and approves what it should escalate. Give an AI agent clear stop conditions: a success stop, effort caps, and the stops that send it back to you.