Agents
From LLMs to Agent Teams
Agents plan, act with tools, cooperate, and remember. GEORGE keeps them aligned and policy-compliant.
Agent vs. LLM vs. RAG
LLM
Reasoning & generation from training data only.
RAG
Retrieval-augmented: injects fresh, external knowledge.
Agent
Decides, plans, calls tools/APIs, and executes workflows.
Building Blocks
- Role-playing: Explicit responsibilities per agent.
- Focus/Tasks: Narrow scope, high reliability.
- Tools: Custom tools & MCP servers for reuse.
- Cooperation: Multi-agent collaboration patterns.
- Guardrails: Validation, limits, fallback/human-in-the-loop.
- Memory: Short-term, long-term, entity memory.
GEORGE (Orchestrator) — Overview
GEORGE translates user intents into coordinated multi-agent plans, governs tool access, enforces guardrails, and maintains memory across flows.
Task Orchestration
Breaks down intents, assigns to domain agents, tracks completion.
Policy & Guardrails
Constraints for cost/safety/data; human-in-the-loop when needed.
Tool & Memory
Registers MCP tools, rate-limits usage; shares scoped context.
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