Agent Workflow Designer

Overview

Workflow Decision Tree

1. Assess the problem scope
   • Is the problem clearly defined? → Proceed to Problem Analysis
   • Is the problem vague? → Ask clarifying questions about desired outcomes and constraints
2. Determine architecture complexity
   • Simple task (single action)? → Single agent with basic tools
   • Complex task (multiple sub-tasks)? → Consider multi-agent orchestration
   • Integration task (connecting systems)? → Focus on Layer 4 (Tooling) design
3. Follow the appropriate workflow
   • New agent from scratch → Apply 9-Step Building Process
   • Existing agent improvement → Focus on specific layers needing enhancement
   • Tool integration problem → Apply MCP and tooling patterns

9-Step Agent Building Process

Step 1: Define Purpose and Scope

• What specific outcome does the end user need?
• What are the constraints (budget, time, resources)?
• What's the success metric?

Step 2: Structure Inputs and Outputs

• Use JSON schemas or Pydantic models, not free text
• Define required vs. optional fields
• Specify data types and validation rules

• Return data objects, not prose
• Define clear error states
• Include confidence scores when relevant

Input: {
  "complaint_text": "string",
  "customer_id": "string",
  "order_id": "string (optional)"
}

Output: {
  "action": "approve_refund | escalate | request_info",
  "refund_amount": "number",
  "reasoning": "string",
  "confidence": "number"
}

Step 3: Write System Instructions

• Role definition: "You are a sales qualification specialist..."
• Behavioral guidelines: "Always ask for budget before proposing solutions"
• Output format requirements: Specify JSON structure, word limits, tone
• Edge case handling: What to do when data is missing or ambiguous

Step 4: Enable Reasoning and External Actions

1. Reason: Analyze the current state and decide next action
2. Act: Call an API, use a tool, or make a decision
3. Observe: Review the result and determine if goal is achieved

• Begin with if/then logic before complex reasoning chains
• Add tools incrementally (don't overwhelm with 50 tools at once)
• Test each tool integration independently

• Calculators for math operations
• Web browsers for research
• Database queries for data retrieval
• API calls to external systems

Step 5: Orchestrate Multiple Agents (When Needed)

• Task has clearly separable sub-tasks
• Different sub-tasks require different expertise
• Parallel processing would improve speed

• Simple linear workflows
• Tasks that require continuous context
• When handoff complexity exceeds benefit

1. Research Agent: Gathers information from sources
2. Analysis Agent: Processes and synthesizes data
3. Writing Agent: Creates structured outputs
4. QA Agent: Reviews quality and accuracy

Step 6: Implement Memory and Context

• What happened this session
• Recent user interactions
• Current task state

• User preferences and settings
• Past interaction patterns
• Historical decisions

• Relevant information from knowledge base
• Similar past cases
• Domain-specific context

• Start with simple conversation buffers
• Add vector databases only when needing semantic search across large datasets
• Consider memory retrieval latency in architecture

Step 7: Add Multimedia Capabilities

• Voice input/output for accessibility
• Image understanding for visual tasks
• Document processing (PDF, DOCX, spreadsheets)

Step 8: Format and Deliver Results

• Clear formatting and structure
• Scannable with headers and bullets
• Professional appearance

• Valid JSON/XML
• Consistent field names
• Error codes for handling

Step 9: Build Interface or API

• Chat interface for conversational tasks
• API endpoints for system integration
• Integration with existing tools (Slack, email, CRM)

8-Layer Architecture Framework

Layer 1: Infrastructure

• GPU/TPU requirements for inference
• Data storage and retrieval speed
• Load balancing for scale
• Monitoring and observability

Layer 2: Agent Internet

Layer 3: Protocol

Layer 4: Tooling Enrichment

• Data retrieval (databases, APIs)
• Computation (calculators, processors)
• Communication (email, messaging)
• Content creation (documents, reports)

Layer 5: Cognition Reasoning

• Guardrails to prevent hallucinations
• Error recovery strategies
• Confidence scoring
• Graceful degradation

Layer 6: Memory Personalization

Layer 7: Application

Layer 8: Ops Governance

• Cost tracking per agent action
• Privacy enforcement and data handling
• Human-in-the-loop for critical decisions
• Audit logs and compliance

Problem-to-Solution Workflow

• What's the current manual process?
• What's the desired outcome?
• What are the constraints (time, cost, technical)?
• What data sources are available?

• Is the task repetitive and rule-based?
• Does it require decision-making with context?
• Would automation provide significant value?
• Is the problem scope clear and bounded?

• Simple task → Focus on Layers 4, 5, 7 (tools, reasoning, application)
• Complex integration → Add Layer 3 (protocol) emphasis
• Scalability concern → Prioritize Layers 1, 8 (infrastructure, ops)

• Critical decision points
• Tool integration requirements
• Multi-agent needs (if any)
• Memory and context strategy

• For PMs: High-level architecture and tool selection
• For engineers: Detailed technical implementation with code patterns
• For product teams: Full stack from requirements to monitoring

Tool Integration Patterns

MCP (Model Context Protocol) Integration

1. Agent discovers available tools
2. Agent calls tools using standardized interface
3. Tool returns structured response
4. Agent processes and continues workflow

Custom API Integration

1. Define clear API contract (inputs/outputs)
2. Implement error handling and retries
3. Add rate limiting and caching
4. Monitor usage and costs
5. Document for agent consumption

Common Integration Scenarios

• Read customer data
• Create/update records
• Search across objects
• Trigger workflows

• Send messages/notifications
• Read incoming requests
• Monitor channels
• Respond to mentions

• Query structured data
• Retrieve documents
• Search knowledge bases
• Aggregate information

Decision Framework: Single vs. Multi-Agent

Use Single Agent When:

• Task is linear and sequential
• Context must be maintained throughout
• Decision-making is unified
• Complexity of orchestration > benefit

Use Multi-Agent When:

• Clear task separation exists
• Sub-tasks need different expertise
• Parallel processing improves performance
• Quality benefits from specialization

Common Pitfalls and Solutions

Pitfall 1: Scope Creep

Pitfall 2: Tool Overload

Pitfall 3: Skipping System Prompt

Pitfall 4: No Error Handling

Pitfall 5: Ignoring Costs

Pitfall 6: Over-Engineering Architecture

Output Format

1. Problem Restatement: Confirm understanding of the user's need
2. Agent Architecture Recommendation: Single vs. multi-agent, with rationale
3. Step-by-Step Workflow: Apply relevant steps from the 9-step process
4. Tool Integration Plan: Specific tools needed and integration approach
5. Layer Analysis: Which of the 8 layers need focus and why
6. Implementation Guidance: Prioritized next steps based on user's role
7. Success Metrics: How to measure if the agent is working

Agent Taxonomy Quick Reference

• Examples: ChatGPT Agent, Claude, Gemini, Grok
• Best for: Quick tasks, research, content creation
• User type: Everyone, especially PMs

• Examples: Zapier Central, n8n, Make
• Best for: Workflow automation without coding
• User type: PMs, operations teams

• Examples: LangChain, CrewAI, AutoGen, Swarm
• Best for: Custom agent features in products
• User type: Engineering teams

• Examples: Cursor (coding), Perplexity (research), Notion AI (writing)
• Best for: Specific job-to-be-done with deep specialization
• User type: Domain-specific professionals