Designing Agent Workflows for Business Processes

Every workflow has decision points where the path branches:

Types of decisions:

Agent-decidable:

• Based on clear criteria the agent can evaluate
• Example: "If order > $100, apply discount"
• Agent has the information and authority

Human-required:

• Policy decisions outside agent scope
• High-stakes irreversible actions
• Edge cases requiring judgment
• Example: "Approve refund over $500"

Rule-based:

• Deterministic logic that doesn't need LLM
• Implement in code, not agent reasoning
• Example: "Route to EU support if country in EU list"

Design principle: Make decisions explicit. Don't let the agent make important decisions implicitly. Define decision points clearly in the workflow.

Most production workflows need human involvement somewhere:

Where to insert humans:

Approval gates:

• Before irreversible actions (sending emails, making charges)
• For high-value decisions
• When confidence is low

Review points:

• Quality check before final output
• Audit sample of automated decisions
• Training data collection

Escalation paths:

• Agent can't handle the case
• User requests human
• Error threshold exceeded

Implementation patterns:

Async approval:

• Agent requests approval
• Waits (or moves to other tasks)
• Resumes when approved

Timed auto-approval:

• Human has X time to review
• Auto-approves if no intervention
• Good for low-risk, high-volume

Human takeover:

• Agent hands off completely
• Human completes the task
• Agent learns from the resolution

Agent workflows must handle failures gracefully:

Types of failures:

Tool failures:

• API errors, timeouts, rate limits
• Strategy: Retry with backoff, try alternative

Reasoning failures:

• Agent misunderstands or goes off track
• Strategy: Detect via validation, reset context

External failures:

• Systems unavailable, data missing
• Strategy: Graceful degradation, notify humans

Error handling patterns:

Retry with backoff: Transient failures often resolve with retry.

Checkpoint and resume: Save state so workflow can resume after fixing issues.

Fallback paths: Alternative approaches when primary fails.

Graceful degradation: Provide partial value even when full completion isn't possible.

Escalation: When recovery isn't possible, escalate to humans with full context.

You must be able to understand what your agent is doing:

What to log:

• Every agent decision and reasoning
• All tool calls with inputs and outputs
• State transitions
• Errors and recoveries
• Human interventions
• Timing information

Tracing structure:

• Unique ID per workflow execution
• Parent-child relationships for nested actions
• Timestamps for performance analysis
• Correlation with user sessions

Monitoring needs:

• Success/failure rates by workflow type
• Time to completion distributions
• Cost per execution
• Human intervention frequency
• Error patterns and trends

Debugging capabilities:

• Replay any workflow step by step
• Understand why agent made each decision
• Compare successful vs. failed runs
• Test changes against historical inputs

Build observability from the start. Retrofitting is painful.