Multi-Agent Layer and Design

Multi-Agent Design
Use role-based agents instead of one omnipotent agent. 
 
 
 
 
 
 Agent 
 
 
 Responsibility 
 
 
 
 
 Planner 
 
 
 Break down tasks 
 
 
 
 
 Researcher 
 
 
 Retrieval 
 
 
 
 
 Executor 
 
 
 Tool use 
 
 
 
 
 Verifier 
 
 
 Check outputs 
 
 
 
 
 Safety Agent 
 
 
 Policy checks 
 
 
 
 
 Finance Agent 
 
 
 Budget controls 
 
 
 
 
 Audit Agent 
 
 
 Logging 
 
 
 
 
 
 Orchestration Patterns 
 ●       Manager-worker — a coordinator agent dispatches to specialist workers and merges results. 
 ●  

Task graph / stateful graph orchestration — the workflow is modeled as a directed (often cyclic) graph with conditional branching, persistent checkpoints, and interruptible human-in-the-loop points, rather than a fixed linear chain. 
 ●       Shared memory store — agents coordinate through a common memory layer instead of direct message passing. 
 

Open problem: Byzantine fault tolerance in adversarial multi-agent settings remains an unresolved research area — don't assume agent-reported results are trustworthy without independent verification in high-stakes settings. 
 Model Routing / Cost Optimization 
 Use the cheapest model that can reliably complete the task. 
 
 
 
 
 
 Tasks 
 
 
 Model Tier 
 
 
 
 
 Classification 
 
 
 Small 
 
 
 
 
 Summaries 
 
 
 Small / Medium 
 
 
 
 
 Planning 
 
 
 Medium / Large 
 
 
 
 
 Coding 
 
 
 Specialist 
 
 
 
 
 Verification 
 
 
 Large 
 
 
 
 
 Legal / Compliance 
 
 
 Premium deterministic 
 
 
 
 
 
 Principle: Intelligence should scale with difficulty. Many current model families additionally expose configurable reasoning effort as a routing dimension in its own right — treat effort level as a tunable dimension alongside model size. 
 Reflection / Critique Loops 
 Use secondary reasoning for high-stakes decisions. 
 Trigger Conditions 
 ●       Low confidence 
 ●       High risk 
 ●       Contradictory evidence 
 ●       Large transaction 
 ●       Compliance-sensitive request 
 Reflection Flow 
 
 Human-in-the-Loop Design 
 Humans should be inserted intelligently. 
 
 
 
 
 
 Human Role 
 
 
 Tasks 
 
 
 
 
 Reviewer 
 
 
 High-risk decisions 
 
 
 
 
 Supervisor 
 
 
 Real-time override 
 
 
 
 
 Trainer 
 
 
 Correct outputs 
 
 
 
 
 Auditor 
 
 
 Compliance review 
 
 
 
 
 
 Confidence Handoff: Escalate when confidence < 0.85–0.90 
  

Recommended use
 What to Use When 
 
 
 
 
 
 Scenario 
 
 
 When to use 
 
 
 
 
 Highest transparency 
 
 
 ReAct + logs 
 
 
 
 
 Fastest cheap execution 
 
 
 Small model routing 
 
 
 
 
 Complex workflows 
 
 
 Multi-agent workflow / task graph 
 
 
 
 
 Safety critical 
 
 
 Verifier + human gate 
 
 
 
 
 Large enterprise memory 
 
 
 Graph + Vector memory 
 
 
 
 
 Heavy integrations 
 
 
 MCP tool ecosystem 
 
 
 
 
 Packaged reusable know-how 
 
 
 Skills layer (New) 
 
 
 
 
 High trust requirements 
 
 
 Policy-first architecture 
 
 
 
 
 Corpus fits in context window 
 
 
 Load-and-ask, skip the retrieval pipeline (New)