Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Design robust communication protocols for multi-agent systems including message schemas, capability advertisement, negotiation protocols, and service discovery mechanisms with practical Python implementations.
Explore multi-agent reinforcement learning (MARL) concepts including reward shaping, cooperative versus competitive strategies, and policy gradient methods for agent teams with practical Python implementations.
Build an end-to-end code generation pipeline using five specialized AI agents — Planner, Coder, Reviewer, Tester, and Deployer — with complete handoff data structures and orchestration logic in Python.
Master Hierarchical Task Network (HTN) planning for AI agents including task decomposition, method selection, plan refinement, and execution monitoring with complete Python implementations.
Compare three fundamental memory sharing architectures for multi-agent systems — blackboard, message passing, and shared vector stores — with implementation patterns, consistency considerations, and performance tradeoffs.
Design agent teams that improve collectively through experience sharing, collective memory, skill transfer, and performance benchmarking. Includes Python implementations for experience replay and cross-agent learning.
Construct a multi-agent research system with four specialized agents — Scientist, Librarian, Analyst, and Writer — that collaborate on source discovery, analysis, and paper generation with complete Python code.
Build comprehensive governance systems for autonomous AI agents including control plane dashboards, kill switches, audit trails, budget enforcement, and human escalation mechanisms with production-ready Python implementations.
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