# [Designing Agent Workflows](https://chatgpt.com/canvas/shared/6825ece10cac819189e14d95e8ecd032)

## Overview

The GPT-4.1 model introduces significant improvements in agentic capabilities, making it ideal for designing multi-turn workflows that rely on persistence, planning, and structured tool interaction. Whether you’re building automated software agents, coding assistants, customer service bots, or task execution systems, designing for success in GPT-4.1 requires careful coordination between prompt design, system instructions, tool usage, and behavior monitoring.

This guide provides a comprehensive framework for designing effective agent workflows using GPT-4.1, detailing structural components, implementation strategies, tool invocation principles, behavioral anchors, and debugging techniques.

Each section can be reused as a design module for your own applications, while contributing to the broader library of effective agent patterns.


## What Is an Agent Workflow?

An agent workflow is a sequence of steps managed by the model in which it:

1. Interprets the user’s task or goal
2. Selects and applies the right tools
3. Iterates until the goal is fully accomplished
4. Manages context, planning, and persistence internally
5. Responds only after verifiable success criteria are met

This process transforms GPT-4.1 from a turn-based assistant into a semi-autonomous task manager.


## Key Model Behaviors That Enable Agent Design

### Literal Instruction Compliance

GPT-4.1 follows instructions with high fidelity. This includes step ordering, constraints, and termination rules. The model is more responsive to direct, formatted behavioral cues than its predecessors.

### Persistent Multi-Turn Context Management

The model maintains internal state across extended interactions. You can program it to persist in a loop, waiting to exit only once defined conditions are met.

### Planning and Reflection

Though not a reasoning-first model, GPT-4.1 can be prompted to externalize plans, reflect on outcomes, and improve with each iteration when prompted properly.

### Integrated Tool Use

The `tools` parameter in the API allows for direct invocation of functional calls (file inspection, patch application, database lookups, etc.) — making agentic behavior verifiable and extendable.


## Core Agent Workflow Template

### 🧩 System Prompt Template

```markdown
# Agent Instructions
You are a multi-step problem-solving agent. Do not terminate until you have fully completed the assigned task.

## Persistence
- Continue until task completion is verified.
- Do not yield to the user before the solution is complete.

## Tool Use
- Use tools to gather information. Do not guess.
- Only proceed with actions when all necessary data is available.

## Planning
- Before taking an action, create a plan.
- After each step, reflect on success/failure.

# Output Format
- Step number
- Action taken
- Result
- Updated plan (if any)

# Final Output
Summarize the solution, include test results if applicable.
```

This format primes GPT-4.1 for proactive execution, tool integration, and termination control.


## Task Archetypes: Common Agent Patterns

| Task Type                | Characteristics                                                        | GPT-4.1 Design Notes                                |
| ------------------------ | ---------------------------------------------------------------------- | --------------------------------------------------- |
| **Code Fixing Agent**    | Requires bug reproduction, patch generation, validation via tests      | Use `apply_patch` tool + persistent reflection loop |
| **Data Lookup Agent**    | Accesses external data via tool calls and summarizes findings          | Tool use must be verified before user response      |
| **Support Agent**        | Answers factual queries with context validation and escalation support | Include step-by-step message plan and constraints   |
| **Document Synth Agent** | Parses, filters, and summarizes from long context                      | Use instructions at top and bottom of prompt        |


## Designing for Persistence

Persistence is the foundation of reliable agent behavior. Without it, the model will default to single-turn chat behavior.

### Design Pattern

```markdown
You must NOT yield back to the user until the task is fully complete.
Check that all steps are verified. Repeat steps as needed. Only stop when all tests pass or instructions say to stop.
```

Reinforce this message early and late in the prompt. In tests, models were 19% more likely to complete complex multi-step tasks when given persistent execution reminders.


## Designing for Tool Use

The most reliable agents use tools for verifiable context access.

### Tool Integration Best Practices

* Register tools in the `tools` parameter of the OpenAI API, not embedded in prompt text.
* Keep tool names simple and descriptive: `run_tests`, `apply_patch`, `lookup_invoice`.
* Provide clear descriptions and optionally list examples in a separate section.

### Example Tool Schema

```json
{
  "name": "apply_patch",
  "description": "Apply a structured diff patch to a file.",
  "parameters": {
    "type": "object",
    "properties": {
      "input": {"type": "string", "description": "The formatted patch text"}
    },
    "required": ["input"]
  }
}
```

### Tool Usage Prompts

```markdown
If you do not have enough context to proceed, pause and use the tools provided.
Do not guess about code, structure, or missing data. Always verify by tool.
```


## Planning and Reflection Prompts

Planning and reflection are the structural anchors of agentic reasoning.

### Pre-Action Planning Prompt

```markdown
Before proceeding:
1. Restate the goal in your own words
2. Write a short plan of how you will solve it
3. List any tools you will need
```

### Post-Action Reflection Prompt

```markdown
After taking an action:
1. Summarize the result
2. List any unexpected outcomes
3. Determine if the goal is met
4. If not, update your plan and try again
```

These templates increase accuracy in multi-step execution by enforcing self-monitoring.


## Full Agent Walkthrough: SWE-bench Example

### System Prompt (Extracted from OpenAI's internal best practices)

```markdown
You are a coding agent tasked with solving bugs in open-source software.

## Task Requirements
- Always plan before you act
- Use tools to inspect files and apply patches
- Validate your fix with rigorous tests

## Workflow
1. Understand the bug deeply
2. Investigate the codebase using tools
3. Plan a fix
4. Apply the fix in small, testable steps
5. Run tests frequently
6. Iterate until all visible and hidden tests pass
7. Reflect and finalize

## Rules
- Do not stop unless all tests pass
- If a fix fails, debug the failure and reattempt
- Document your process
```

### Tool Call: `apply_patch`

```bash
apply_patch <<"EOF"
*** Begin Patch
*** Update File: src/module/handler.py
@@ def process_input():
-    if input:
+    if input is not None:
     handle(input)
*** End Patch
EOF
```

### Tool Call: `run_tests`

```bash
!python3 -m unittest discover
```

### Agent Response (Example)

```
Step 1: Analyzed stack trace and confirmed null check missing
Step 2: Planned patch using apply_patch tool
Step 3: Applied patch successfully
Step 4: Ran tests – 1 failure in edge case
Step 5: Revised patch
Step 6: Re-ran tests – all passed
Task complete. Final patch validated.
```


## Failure Modes and Mitigations

| Failure Type                   | Symptom                               | Mitigation Tip                               |
| ------------------------------ | ------------------------------------- | -------------------------------------------- |
| Premature Turn Yield           | Agent ends before solving             | Add explicit no-yield rules in system prompt |
| Tool Hallucination             | Tool called with invalid or null data | Instruct agent to ask for data if missing    |
| No Planning or Reflection      | Skips step-by-step reasoning          | Add planning and reflection anchors          |
| Ignoring Final Validation Step | Says task complete before verifying   | Add final verification checklist to prompt   |


## Output Format Suggestions

A consistent output format improves interpretability and downstream usage.

### Recommended Layout

```markdown
# Task Status: In Progress

## Current Step: Plan and Execute Fix
- Tool used: apply_patch
- Patch outcome: Success

## Next Step
- Run full tests
- Validate output for edge cases
```

You can train GPT-4.1 to adopt consistent internal status reporting with a format guide provided in each system prompt.


## Escalation, Recovery, and Termination

### Escalation

Encourage the model to escalate to the user when required:

```markdown
If more data or permissions are needed, ask the user explicitly.
If a step cannot be completed after three attempts, escalate.
```

### Recovery

Allow the model to acknowledge failure and retry with adjustments:

```markdown
If your fix fails tests, reflect and revise the patch.
List new hypotheses and retry using a modified plan.
```

### Termination

Use clear termination rules:

```markdown
Only end your session when:
- All tests pass
- The task is fully verified
- You have summarized your actions for the user
```


## Behavioral Design Tips

| Technique                     | Effect                                              |
| ----------------------------- | --------------------------------------------------- |
| System prompt layering        | Prioritizes stable task framing                     |
| Mid-prompt behavior resets    | Reinforces correct tool usage after failed attempts |
| Named sections (Markdown/XML) | Improves adherence to plan and formatting           |
| Soft conditionals             | Encourages resilience (“If X fails, try Y…”)        |


## Designing for Developer Control

Create parameterized prompts for easier tuning and behavior adjustment.

### Template with Parameters

```python
AGENT_TEMPLATE = f"""
# Role: {role}
# Task: {task_description}
# Output Format: {format_spec}
# Tools: {', '.join(tool_names)}
# Planning Required: {'Yes' if planning else 'No'}
"""
```

Use this model to power dashboards, agent templates, and UI-driven behavior controls.


## Testing Agent Workflows

Use evaluation harnesses to test agent performance:

* Track step completion
* Analyze tool usage logs
* Compare plan quality across variants

Key metrics:

* Task success rate
* Iteration count per completion
* Tool error frequency
* Response length and structure fidelity


## Summary

Agent workflows in GPT-4.1 are structured, reliable, and controllable — provided the design follows consistent instruction patterns, plans for tool usage, and includes persistence logic.

Follow these principles:

* Anchor every agent with clear, literal instructions
* Use tool APIs instead of embedded tool descriptions
* Require planning and reflection around actions
* Validate every output with structured criteria

By shaping agent workflows as formal task managers, developers can build systems that reliably complete complex operations in a safe, verifiable manner.


## Next Steps

Explore these additional modules to expand your agent capabilities:

* [`Prompting for Instruction Following`](./Prompting%20for%20Instruction%20Following.md)
* [`Long Context Strategies`](./Long%20Context.md)
* [`Tool Calling and Integration`](./Tool%20Use%20and%20Integration.md)

For more agent-ready templates, visit the `/agent_design/` directory in the main repository.


For contributions or questions, open an issue or submit a pull request to `/agent_design/Designing Agent Workflows.md`.