Agentic Workflows

Definition: Agentic Workflows are advanced automations that incorporate AI agents for intelligent decision-making. Instead of rigid if-then rules, agentic workflows can reason, adapt, and handle complex situations.

What Makes Workflows "Agentic"?

Traditional Automation:

• Fixed rules and conditions
• Breaks when unexpected situations arise
• Requires manual updates for new scenarios
• Limited to predefined paths

Agentic Workflows:

• AI reasoning at decision points
• Adapts to unexpected situations
• Learns from outcomes
• Handles edge cases gracefully

Agentic Capabilities

Intelligent Routing:
AI agents analyze incoming requests and route them based on understanding, not just keywords.

Contextual Decisions:
Agents consider full context - history, relationships, urgency - when making workflow decisions.

Adaptive Responses:
When workflows encounter unexpected situations, agents can reason through solutions.

Learning from Outcomes:
Successful resolutions inform future decisions, making workflows smarter over time.

Building Agentic Workflows

Step 1: Identify Decision Points
Where in your workflow do humans currently need to make judgment calls?

Step 2: Train Your Agents
Give agents the context and examples they need to make good decisions.

Step 3: Set Guardrails
Define boundaries for agent decisions and escalation triggers.

Step 4: Monitor and Refine
Review agent decisions and provide feedback to improve.

Agentic Workflow Patterns

Intelligent Triage:

• Request arrives
• Agent analyzes content, sentiment, urgency
• Routes to appropriate handler
• Provides context and recommendations

Adaptive Onboarding:

• New user signs up
• Agent assesses needs and experience level
• Customizes onboarding path
• Adjusts based on engagement

Smart Escalation:

• Issue detected
• Agent attempts resolution
• Escalates with full context if needed
• Learns from resolution for future

Agent Integration Points

Decision Nodes:
Replace simple conditions with agent reasoning

Content Processing:
Agents analyze, summarize, or transform content

Communication:
Agents draft responses or recommendations

Quality Assurance:
Agents review outputs before final actions

Best Practices

Start with High-Volume Decisions:
Focus on decisions that happen frequently with clear success criteria.

Maintain Human Oversight:
Keep humans in the loop for high-stakes decisions.

Measure Decision Quality:
Track outcomes to ensure agent decisions are improving.

Iterate Based on Feedback:
Regularly review and refine agent training.

Related Wiki Pages: AI Agents, Vibe Workflows, Living Trinity