Autonomous Multi-Agent Teams & AI Coaches Architecture
Collaborative multi-agent swarm architecture where specialized AI personas collaborate to research, critique, and coach users in real time.
Parallel autonomous agent collaboration cuts multi-step task completion time
Tailored agents for strategy, technical review, tone calibration, and feedback
Personalized coaching feedback based on historical user interactions
Project Architecture & Objectives
Key Capabilities Delivered
Engineered cyclic state-graph workflows with LangGraph to orchestrate multi-agent turn taking and validation checks
Built interactive Next.js web interface visualizing agent reasoning chains and intermediate thoughts in real time
Configured Redis Pub/Sub backplane for asynchronous message distribution and agent coordination
Implemented persistent conversational memory with vector recall to track long-term user development goals
Engineering Challenges & Solutions
Critical architectural hurdles solved to deliver performance, security, and scale.
Agent Loop Drift and Hallucination Feedback
When autonomous agents converse, errors in early steps compound into irrelevant debates.
Introduced a deterministic supervisor agent with rigorous evaluation heuristics and early stopping conditions.
Visual Systems & User Experience
Technologies & Infrastructure
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