Direct answer
Use this builder to turn an agent idea into a structured system prompt with role, tools, memory, fallback policy, output format, and eval criteria.
Tier-1 support agent prompt
Input: Role, account lookup tool schema, escalation fallback, structured JSON output, and support success criteria.
Output: The output should generate framework-ready prompt text and deterministic lint warnings for missing sections.
How to use this tool
- 1. Describe the agent role.
- 2. Add tools and JSON schemas.
- 3. Choose memory and fallback policy.
- 4. Copy the generated framework-specific prompt.
Why agent prompts are not chatbot prompts
A chatbot prompt can define tone and response style. An agent prompt must define role boundaries, tools, tool-use rules, memory policy, fallback behavior, output schema, and evaluation criteria.
Ambiguity becomes operational risk. If the prompt does not say what to do when a tool fails, the agent improvises. Production systems need fewer improvisations and more explicit contracts.
What the lint checks for
The lint pass checks for missing role, weak success criteria, absent fallback policy, no output format, missing examples, and invalid-looking JSON schema. It is deterministic and local, not an LLM-generated review.
Assumptions and methodology
This tool uses transparent browser-side calculations and curated assumptions rather than LLM-generated recommendations. Outputs are planning estimates. They should be validated against provider pricing, production traces, engineering quotes, or domain review before money, compliance, safety, or hiring decisions are made.
Numerical defaults are dated and surfaced on the page. The methodology favours explicit assumptions over false precision: every estimate is meant to expose the variable that drives the result, not to pretend that early planning data is exact.
Turn the result into an implementation plan
Bring the scenario to a strategy call and I will pressure-test the workflow, assumptions, failure modes, and delivery path.
Book a strategy callFrequently asked questions
- What is different from a chatbot prompt?
- An agent prompt includes tool rules, state assumptions, fallback policy, output constraints, and eval criteria. A chatbot prompt usually focuses on conversational style and task framing.
- How do I write a good tool description?
- A good tool description states when to use the tool, required arguments, what the tool returns, and when not to use it. The agent should not infer tool policy from names alone.
- What memory strategy should I use?
- Use no memory for stateless tasks, sliding windows for short sessions, summaries for long conversations, vector memory for retrieval, and hybrid memory when both history and knowledge matter.
- How accurate are token counts?
- This lightweight implementation estimates tokens at roughly four characters per token. Exact model-specific tokenizers should be added before using token counts for billing-critical work.
- Does this work for any model?
- The prompt structure works broadly, but tool syntax differs across providers and frameworks. Always verify generated formats against current framework docs before shipping.
- What does the lint check for?
- The lint checks whether the prompt has a clear role, success criteria, output format, fallback policy, examples, and valid-looking tool schemas. It catches common omissions, not semantic correctness.