AI agent design planner
Most agent projects fail not at coding but at design: an unclear goal, unbounded tool access, no memory strategy, and no escalation path. This planner walks you through the decisions that matter — goal, memory, tools, guardrails, and evaluation — and turns them into a structured prompt that produces a complete agent design document you can review before writing any code.
The five design decisions that determine whether your agent works
1. Goal clarity
An agent needs exactly one primary goal expressed as a measurable outcome. “Help users with customer service” is a mission, not a goal. “Resolve a support ticket by finding a matching help article and, if none exists, escalating to a human agent with a summary” is a goal. Agents with fuzzy goals drift, loop, and take unexpected actions because they are filling gaps in their instructions with their own judgement.
2. Memory model
Stateless agents are simpler and cheaper. They work well for single-turn tasks where context does not need to carry over. Short-term memory (context window accumulation within a task) works for multi-step tasks where the agent needs to remember earlier tool outputs. Long-term memory (a vector store or database persisted between sessions) is only worth the added complexity when the agent genuinely needs to learn or reference history across separate tasks. Defaulting to stateless unless long-term context is explicitly required avoids an entire class of bugs and privacy issues.
3. Tool scope and least privilege
Each tool the agent can call should have an explicitly defined scope: what it can read, what it can write, what it cannot touch, and what happens when it fails. An agent with broad file-system or API access will eventually use it in ways you did not anticipate. Designing least-privilege boundaries per tool before coding means the agent’s worst-case surface is bounded from the start.
4. Escalation path
Every agent needs at least one clearly defined escalation trigger — a condition under which it must stop and hand control to a human. Without this, a stuck or confused agent loops indefinitely. Common triggers include: confidence below a threshold, an irreversible action, exceeding a cost or resource limit, or anything the agent cannot match to a known tool.
5. Evaluation plan
Define how you will know the agent works before you build it. What does a successful run look like? What constitutes failure? What metrics will you track? Agents are hard to unit-test in the traditional sense; your evaluation plan identifies the test cases, expected outputs, and failure modes to probe.
How it works
You define the agent’s single primary goal, list the tools it may call, choose a memory model, set the output format, and specify when it must escalate to a human. The tool assembles a prompt that instructs an LLM to expand those choices into a full design doc: architecture overview, the control loop, tool contracts with least-privilege boundaries, memory schema, guardrails, escalation rules, and an evaluation plan. Everything is generated locally in your browser.
Tips and notes
- One primary goal. Agents that chase several goals at once become unpredictable; scope to one and add others later as separate agents or tools.
- Least privilege per tool. Define exactly what each tool can touch and what happens on failure — unbounded tool access is the most common agent risk.
- Always have an escalation path. Irreversible or high-stakes actions should require human confirmation; the design doc forces you to name those triggers.
- Plan evals first. Decide how you will measure success before building, or you will not know whether the agent works.