Illustrates an AI agent orchestration pipeline, detailing task assignment, iterative agent work, confidence aggregation, validation, and feedback loops for
graph TD
Start[Coordinator Starts] --> StoreContext[Store Context in Redis]
StoreContext --> |Transfer Point 1| SpawnL3[Spawn Loop 3 Agents]
SpawnL3 --> |Context Injection| L3Work[Loop 3: Agent Work]
L3Work --> |Transfer Point 2| StoreOutput[Store Output + Confidence in Redis]
StoreOutput --> L3Exit[Agent Exit]
L3Exit --> CollectConf[Collect Confidence Scores]
CollectConf --> |Transfer Point 3| ExtractFeedback[Loop 2: Extract Feedback]
ExtractFeedback --> |Transfer Point 4| Aggregate[Loop 3: Aggregate Confidence]
Aggregate --> |Transfer Point 5| GateCheck{Gate Check<br/>Threshold ≥ 0.85?}
GateCheck -->|Fail| StoreFeedback[Store Feedback in Redis]
StoreFeedback --> SpawnFresh[Spawn Fresh Loop 3 Agents]
SpawnFresh --> L3Work
GateCheck -->|Pass| SpawnValidators[Spawn Loop 2 Validators]
SpawnValidators --> |Context Injection| ValidatorReview[Loop 2: Review]
ValidatorReview --> ValidatorExit[Validator Exit]
ValidatorExit --> CollectConsensus[Collect Consensus Scores]
CollectConsensus --> ConsensusCheck{Consensus<br/>Reached?}
ConsensusCheck -->|No| StoreFeedback
ConsensusCheck -->|Yes| HandoffPO[Transfer Point 6: Handoff to Product Owner]
HandoffPO --> |Context Injection| PODecision[Product Owner Decision]
PODecision --> |Transfer Point 7| FinalStorage[Store Final Output]
FinalStorage --> Complete[Task Complete]
style GateCheck fill:#f9f,stroke:#333,stroke-width:2px
style ConsensusCheck fill:#f9f,stroke:#333,stroke-width:2px
style StoreOutput fill:#bbf,stroke:#333,stroke-width:2px
style FinalStorage fill:#bfb,stroke:#333,stroke-width:2pxThis diagram visualizes a complete data flow pipeline for an AI agent orchestration system. It details the process from a coordinator initiating tasks, spawning Loop 3 agents for work, storing their outputs and confidence scores in Redis, and then entering a feedback loop (Loop 2) for validation. Key elements include gate checks based on confidence thresholds, iterative refinement through feedback, spawning validators, collecting consensus, and finally, a handoff to a product owner for decision-making before task completion. It highlights 7 critical data transfer points throughout the pipeline.
Use this diagram when designing complex AI agent systems requiring iterative processing, confidence-based gating, human-in-the-loop validation, and robust feedback mechanisms. It's ideal for illustrating workflows where tasks are broken down, executed by agents, and refined through multiple stages of review and consensus.
Adapt this pipeline by modifying the number and type of agent loops (e.g., adding a Loop 1 for pre-processing), adjusting confidence thresholds for gate checks, integrating different storage solutions beyond Redis, or customizing the validation and product owner decision stages. The feedback loop can be expanded to include more detailed remediation steps or automated re-tasking based on specific failure modes.