Agent Ignores JSON Output Format Instruction — Returns Prose Instead of JSON

Symptom

Prompt says “respond in JSON” but agent returns natural language
Sometimes returns JSON inside markdown code fences (json ...), causing json.loads() to fail
Occasionally returns a mix: explanation text followed by JSON
Behavior is inconsistent — sometimes works, sometimes doesn’t

Root Cause

Vague format instructions lose to the model’s default preference for natural language. Without an explicit structure + example + enforcement, the model defaults to whatever feels most natural for the content type. Code fences appear because the model was trained to present code/JSON in fenced blocks.

Fix

Step 1: Add explicit constraint + schema + example

# WEAK (often ignored)
"Respond in JSON."

# STRONG
"Your response MUST be a valid JSON object with NO other text.
Schema:
{
  "status": "success" | "error",
  "result": <string>,
  "confidence": <number 0.0-1.0>
}

Example response:
{"status": "success", "result": "Fixed the auth error", "confidence": 0.9}

Do NOT include markdown code fences. Do NOT add explanation text before or after the JSON."

Step 2: Parse defensively with fence stripping

import json, re

def parse_agent_json(response: str) -> dict:
    # Strip markdown fences if present
    response = response.strip()
    match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', response)
    if match:
        response = match.group(1).strip()

    # Strip any leading/trailing prose
    json_start = response.find('{')
    json_end = response.rfind('}') + 1
    if json_start >= 0 and json_end > json_start:
        response = response[json_start:json_end]

    return json.loads(response)

Step 3: Add a retry with explicit correction

async def get_json_response(prompt, max_retries=2):
    for attempt in range(max_retries + 1):
        response = await agent.complete(prompt)
        try:
            return parse_agent_json(response)
        except (json.JSONDecodeError, ValueError) as e:
            if attempt == max_retries:
                raise
            prompt += f"\n\nYour previous response was not valid JSON: {response[:200]}\nRespond with ONLY a valid JSON object, nothing else."

Step 4: Use tool_use / structured outputs when available

For Anthropic API, use tool_use to enforce output structure:

tools = [{
    "name": "return_result",
    "description": "Return the structured result",
    "input_schema": {
        "type": "object",
        "properties": {
            "status": {"type": "string", "enum": ["success", "error"]},
            "result": {"type": "string"},
            "confidence": {"type": "number"}
        },
        "required": ["status", "result", "confidence"]
    }
}]
# Model is forced to call the tool with valid JSON — no format failures

Expected Token Savings

Debugging JSON format failures across multiple turns: ~6,000 tokens This fix: ~400 tokens to implement

Environment

Anthropic Claude / any LLM with function calling
Source: direct experience with production agent outputs

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