Workflow-Based Architecture

Overview

Workflow-based systems are designed for complex, multi-step business processes that require orchestration, audit trails, and human-in-the-loop capabilities. They prioritize reliability, traceability, and deterministic execution over real-time responsiveness.

Core Concepts

Orchestrator Agent

Owns the plan/state for the entire workflow
Pushes steps to workers based on workflow definition
Manages state transitions and error recovery
Coordinates human-in-the-loop interventions

Workers/Skills

Specialized agents for specific tasks
RAG agents for knowledge retrieval
Finetuned writers for content generation
Analysis agents for data processing
Notification agents for communication

State Manager

Slot/state machine with retries
Idempotent steps for reliability
Compensation logic for rollbacks
State persistence across failures

Memory System

Shared store for run context
Artifact storage for intermediate results
Cross-step data sharing
Audit trail maintenance

Eval Gates

Step-level quality checks
Human review queues when below threshold
Automated retry logic
Escalation policies for failures

Example Workflow: Document Q&A Pack

Step 1: Ingest

Parse document format (PDF, Word, etc.)
Extract text and metadata
Chunk content for processing
Store in RAG system with provenance

Step 2: Build Brief

Use finetuned model for summarization
Apply post-guard schema validation
Generate structured brief
Store as workflow artifact

Step 3: Answer Set Generation

RAG agent retrieves relevant information
Generate Q&A pairs based on content
Cite sources with proper attribution
Validate answer quality

Step 4: Package

Combine brief and Q&A into final format
Generate PDF/HTML artifacts
Apply branding and formatting
Create distribution packages

Step 5: Quality Gate

Apply rubric: coverage, accuracy, tone
If quality below threshold → human review task
Automated retry with different parameters
Escalation to senior reviewers

Step 6: Notify

Send webhook/email notifications
Update external systems
Persist run record and metrics
Trigger downstream processes

Non-Functional Requirements

Reliability

Exactly‑once effects via idempotency keys
Durable queue for step persistence
Compensation logic for rollbacks
Circuit breakers for external services

Observability

Run timeline visualization
Per‑step spans for debugging
Eval dashboards for quality monitoring
Cost tracking per workflow run

Compliance

Artifact retention windows
PII minimization strategies
Audit trails for all actions
Data lineage tracking

Building Block Behavior

Prompts

Step‑scoped, deterministic templates
Strict schema outputs for validation
Low temperature for consistency
Reproducible across runs

Agents

Orchestrator + worker agents pattern
Plan across steps with state management
Strong pre/post processors for state transforms
Hooks like beforeStep/afterStep and onRetry

LLM Guards

Hard gates at step boundaries
Retry/human review on failure
Decisions audited with reasons
Quality thresholds enforced

Evals

Gate each step with quality checks
Regression packs for testing
Quality budgets for monitoring
Automatic stop/fix loops

RAG

Stage‑specific retrieval for each step
Ingestion ahead of time for performance
Evidence committed as artifacts
Re‑ranking tuned for accuracy

Memory

Run context + artifact store
Durable TTL for persistence
Cross‑run caches and dedup keys
State sharing between steps

Operational Concerns

Exactly‑once effects via idempotency
Queue health monitoring
Idempotency audits for data integrity
Run timelines and step SLAs
Cost optimization across workflow runs

Common Patterns

Human-in-the-Loop

Quality gates with human review
Escalation policies for complex cases
Approval workflows for sensitive operations
Feedback loops for continuous improvement

Error Handling

Retry with exponential backoff
Compensation logic for rollbacks
Dead letter queues for failed steps
Manual intervention capabilities

State Management

Immutable state transitions
State snapshots for debugging
Rollback capabilities
State validation at each step

Parallel Processing

Independent step execution
Resource pooling and optimization
Dependency management
Result aggregation

Monitoring & Debugging

Step-by-step execution traces
Performance metrics per step
Error analysis and reporting
Capacity planning insights

Workflow Design Best Practices

Step Granularity

Keep steps focused and testable
Balance between granularity and overhead
Design for independent execution
Plan for error isolation

State Design

Immutable state transitions
Clear data contracts between steps
Version state schemas
Plan for state migration

Error Recovery

Design for partial failures
Implement compensation logic
Plan for manual intervention
Test failure scenarios

Performance

Optimize for throughput over latency
Use parallel execution where possible
Implement intelligent caching
Monitor resource utilization

Next Steps

Review the Chatbot-Based Architecture to understand the alternative approach
Check the Architecture Comparison for detailed trade-offs
Start with workflow definition and step specifications
Plan your monitoring and observability strategy early
Design for failure and human intervention from the start

Workflow-Based

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