# Building Agentic Systems
All Building Agentic Systems notes →1. Why Multi-Agent Systems
Covers the concrete failure modes of single-agent systems, such as context overload, tool sprawl, and conflicting objectives, that motivate splitting work across multiple specialized agents.
2. Collaboration Models
Splitting one investigation agent into metrics, logs, and traces specialists — tool isolation and prompt specialization as the design levers that make each one reliable.
3. Communication Protocols
Agent-to-agent protocols, shared memory, message passing, coordination patterns, and how a multi-agent system recovers when one agent in the chain fails.
4. Task Decomposition
Covers strategies for breaking a complex goal into subtasks that can be assigned to different agents, and how decomposition granularity affects coordination overhead.
5. Agent Negotiation
Covers how agents with different objectives or partial information reach agreement on a shared action, including bidding and argumentation-based negotiation protocols.
6. Consensus Mechanisms
Covers how multi-agent systems reach agreement on a single output or decision when individual agents disagree, drawing on voting, quorum, and distributed-consensus analogies.
7. Swarm Intelligence
Covers decentralized multi-agent patterns where global behavior emerges from simple local rules rather than centralized planning, and where that tradeoff pays off for agentic systems.
8. Distributed Coordination
Covers coordinating agent state and actions across distributed processes, including the partial failure, message loss, and race condition modes borrowed from distributed systems theory.
9. Supervisor Architectures
A supervisor agent that delegates to the specialist agents, aggregates their results, resolves conflicting conclusions, and generates the final incident report.
10. Agent Meshes
Covers service-mesh-inspired architectures for agent-to-agent discovery, routing, and observability at the scale of dozens of interacting agents.
11. Agent Lifecycle Management
Covers how agents are added to, updated or reconfigured within, and retired from an already-running multi-agent workflow -- versioning agent definitions, draining vs. hard-cutting over in-flight runs, and preserving auditability without breaking workflow continuity, using GitHub Copilot's custom agent files as the reference implementation.