// multi_agent_systems
Six capability stages trace the full arc from tool-calling LLMs to autonomous agent-to-agent networks operating at machine speed inside the enterprise.
6
capability stages
tool call → A2A
4
frameworks compared
LangGraph · CrewAI · AutoGen · MCP
N×M→N+M
integration reduction
via Model Context Protocol
∞
scale ceiling
agent-to-agent delegation
// key_findings
Tool calling is the inflection: 2023 was the year every major LLM natively supported structured function invocation, making deterministic automation viable without fine-tuning.
ReAct (Reason + Act) solved the loop problem: agents can observe tool outputs and re-plan mid-execution. LangGraph made this production-grade with typed state machines.
MCP eliminates the N×M integration matrix. Every enterprise tool written once as an MCP server is instantly available to every MCP-compliant agent. The protocol is the USB standard for AI.
A2A (Agent-to-Agent) is the virtual firm. Humans delegate goals, not tasks. Orchestrators decompose intent, specialists execute, synthesisers consolidate, all at machine latency.
The unsolved problem is observability, not capability. Knowing what an agent chain did, why, in what order, and with what confidence is the production-readiness gap.
Financial services is the highest-stakes test bed: compliance, audit trail, PII handling, and determinism requirements force rigour that makes general-purpose agents mature faster.
// data_lineage
// built_with
// methodology_note
Agent topologies were modelled from public framework documentation and enterprise deployment patterns. No proprietary system diagrams reproduced.