AUN Planning Benchmark

x402

AUN Planning Benchmark is a paid API for AI agents from a-upgradenet.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-23).

Runs a structured planning benchmark against an AI agent's system prompt and returns a scored report with skill-level pass/fail analysis and improvement recommendations.

Price / call$0.1

Seller volume →—

Success—

StatusUnknown

How this service works

Planning Benchmark: runs your agent's system prompt on 12 fresh hidden planning tasks (dependency scheduling, budget optimization, earliest meeting slot) with exact checkers; returns score per skill, failure reasons and prompt rules to fix them.

Output

Returns a JSON report with an overall planning score (0–100), total tasks run, number passed, per-skill breakdowns (budget, meeting, schedule) with individual pass rates, a list of specific task failures with reasons, and actionable recommendations for improving each underperforming skill.

Example use cases

Diagnosing a budgeting agent's weaknesses

“My budgeting assistant keeps making suboptimal allocation decisions. Can you run the AUN planning benchmark on its system prompt and tell me specifically where it's failing on budget tasks and what I should fix?”

Comparing agent versions before deployment

“I've just rewritten my scheduling agent's system prompt. Run the planning benchmark on it so I can see the score and skill breakdown — especially for schedule and meeting skills — before I push it to production.”

Validating a new agent's planning readiness

“I'm building a new AI assistant for project coordination. Can you benchmark its system prompt against the planning suite and give me the full report — score, pass rate per skill, and any recommendations?”

Technical details

Endpoint

POSThttps://a-upgradenet.com/x402/planning-benchmark?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a structured, quantitative evaluation of an AI agent's planning capabilities broken down by skill (budget, scheduling, meeting). It is especially useful before deploying or upgrading an agent, or when diagnosing specific planning skill failures. Prefer it over generic LLM self-evaluation when you need reproducible, scored benchmark results with explicit pass/fail task details.

Side effects

System prompt exceeds 8000 character limit — request rejected
Malformed or missing system_prompt field — validation error
Payment not received or insufficient USDC — 402 Payment Required
Server-side benchmark timeout — partial or no results returned
Empty or trivially short system prompt — low-quality benchmark results

Input

Schema · Request body

Field	Type	Description
system_prompt	string	Your agent's system prompt (max 8000 chars). Never include secrets.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "report": {
      "score": 67,
      "total": 12,
      "passed": 8,
      "by_skill": {
        "budget": {
          "rate": 0.5,
          "total": 4,
          "passed": 2
        },
        "meeting": {
          "rate": 0.75,
          "total": 4,
          "passed": 3
        },
        "schedule": {
          "rate": 0.75,
          "total": 4,
          "passed": 3
        }
      },
      "failures": [
        {
          "task": "b2",
          "skill": "budget",
          "reasons": [
            "Total value 14; the best possible is 17."
          ]
        }
      ],
      "recommendations": [
        {
          "skill": "budget",
          "advice": "…"
        }
      ]
    },
    "status": "completed"
  }
}

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