What makes an agent prompt different
An agent system prompt is a control surface. It defines role, tool boundaries, memory, fallback behaviour, output shape, and evaluation criteria. A chatbot prompt can be loose; an agent prompt needs operational discipline.
What makes an agent prompt different
Agent prompts have to tell the model when to act, when not to act, what tools exist, what arguments are valid, what memory can be trusted, how to recover from failure, and what output format downstream systems expect. Ambiguity becomes production risk.
Common mistakes in agent prompts
Common mistakes include vague roles, missing tool schemas, no fallback policy, no examples, no confidence threshold, and free-form output where structured JSON is required. The lint panel focuses on those issues because they create the most frequent failures in early agent builds.
Assumptions and methodology
The builder composes deterministic prompt sections and runs simple lint checks for missing role, tools, fallback, output, and examples.
The page keeps the interaction fast, accessible, and dependency-light while preserving the important planning behaviour from the implementation plan: editable inputs, visible outputs, no signup gate, structured data, internal links, citations, and clear caveats. Heavy runtime features such as Monaco, solc, exact tokenizers, or PDF export should remain code-split when added so the public page stays fast.
Sources and review cadence
Assumptions should be reviewed quarterly, and pricing-sensitive assumptions should be reviewed monthly. The public data files in the repo include last-reviewed or last-verified dates where the plan calls for them.
Need the architecture behind the number?
Use this tool to narrow the conversation, then bring the scenario into an architecture review if the decision affects budget, security, compliance, or production reliability.
Book a strategy callFrequently asked questions
What is the difference between a chatbot prompt and an agent prompt?
A chatbot prompt mainly shapes a response. An agent prompt shapes behaviour across tools, memory, retries, and output contracts. It should define what the agent may do, what it must not do, and how it should fail safely.
How do I write a good tool description?
A good tool description names the purpose, required inputs, side effects, permission limits, and failure mode. The agent should know when to call the tool and when not to. Ambiguous tool descriptions cause unnecessary calls and unsafe actions.
What memory strategy should I use?
Use no memory for stateless tasks, sliding windows for short conversations, summaries for long support interactions, vector memory for retrieval-heavy work, and hybrid memory only when the team can evaluate and debug stale context.
How long should a system prompt be?
Long enough to remove ambiguity, short enough to keep cost and attention under control. Most production prompts should be structured by section and tested with eval cases instead of expanded with generic policy prose.
How accurate are token counts?
The lightweight page uses an approximation suitable for planning. Exact counts require provider-specific tokenizers, and Anthropic or Gemini counts may differ from OpenAI-style estimates. Treat the number as a budget signal, not an exact bill.
What does lint check for?
Lint checks for missing role clarity, missing tool definitions, weak fallback policy, missing output format, and lack of examples. It does not prove the prompt is safe; it identifies issues worth fixing before eval testing.