I'm building an agent workflow and want to debug each step end to end

A good way to debug an agent workflow end to end is to make each step observable, replayable, and attributable.

Recommended debugging setup

1) Break the workflow into explicit stages

Example:

• input received
• plan generated
• tool selected
• tool executed
• result parsed
• final response produced

For each stage, log:

• stage name
• timestamp
• input payload
• output payload
• tool name / model name
• latency
• errors / retries

2) Add a trace ID to every request

Use one trace_id across the entire workflow so you can follow a single run through all steps.

Example log fields:

{
  "trace_id": "run_123",
  "step": "tool_execution",
  "input": {...},
  "output": {...},
  "duration_ms": 184
}

3) Store intermediate artifacts

Save:

• raw model prompts and completions
• tool call arguments
• tool responses
• parsed/normalized results
• final answer

This lets you replay the same execution later.

4) Build a step-by-step replay mode

A replay mode should let you:

• rerun from any step
• inject mocked tool outputs
• compare actual vs expected outputs
• pause before/after each tool call

5) Use structured logging, not plain text

Prefer JSON logs or event records so you can filter by:

• trace_id
• step
• error type
• tool name

6) Add assertions at every boundary

Check assumptions between steps:

• input schema valid
• tool args serializable
• tool output contains expected keys
• parsed response matches contract

7) Capture failures with context

On errors, record:

• failing step
• exception stack
• last valid state
• upstream inputs
• retry count

8) Create a trace viewer

Even a simple UI or notebook that shows:

• step sequence
• inputs/outputs
• timings
• errors
• retries

helps a lot during debugging.

---

Minimal implementation pattern

A simple pattern is wrapping every step with a tracing decorator:

def traced_step(step_name):
    def decorator(fn):
        def wrapper(*args, **kwargs):
            start = time.time()
            try:
                result = fn(*args, **kwargs)
                log_event({
                    "step": step_name,
                    "status": "success",
                    "input": {"args": args, "kwargs": kwargs},
                    "output": result,
                    "duration_ms": int((time.time() - start) * 1000),
                })
                return result
            except Exception as e:
                log_event({
                    "step": step_name,
                    "status": "error",
                    "input": {"args": args, "kwargs": kwargs},
                    "error": str(e),
                    "duration_ms": int((time.time() - start) * 1000),
                })
                raise
        return wrapper
    return decorator

---

If you’re using tools / LLM calls

Also log:

• system prompt
• user prompt
• retrieved context
• tool schema
• model parameters
• raw model output
• tool call payloads

This is usually where hidden bugs live.

---

Best practices

• Keep a debug mode separate from production mode
• Redact secrets before logging
• Use deterministic seeds where possible
• Mock external APIs in tests
• Create golden test cases for common paths and failure paths

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If you want, I can help you design:

1. a trace schema,
2. a Python wrapper/decorator for step-by-step tracing, or
3. a LangChain / OpenAI Agents debugging setup.