Multi-Agent Collaboration Patterns

Published 2026-04-18Updated 2026-10-2944 min read

📅 Date: 2026-04-18 | ⏱️ Reading Time: ~15 minutes

1. Why Multi-Agent?

Single LLM agents quickly reveal their limitations as domains expand and tool usage becomes complex. Key limitations observed in production environments include:

Context Window Saturation: Even a 1M-token-class context window can be exhausted quickly in large monorepos and long sessions, and key context can be lost during summarization. Check each model's context size in the provider's documentation (the 1M-token figure for Claude Opus 4.7 has no verified primary source as of 2026-10-29).
Tool Sprawl: The more MCP tools are connected to a single agent, the more tool-choice accuracy can drop. "20+" is the author's workload-dependent hypothesis, not a fixed limit; confirm the actual threshold with a benchmark on your own workload. The cited Anthropic 2024 "Building effective agents" recommends designing tool definitions carefully but does not give a threshold for a specific number of tools.
Expertise Range Limits: Code review, SQL writing, and security analysis require different system prompts and few-shot configurations. Hard to satisfy all with one prompt.
Cost-Accuracy Tradeoff: Using Opus-class models for all subtasks explodes costs; using only Haiku-class increases failure rate in complex reasoning.

Multi-agent systems solve these limitations through role decomposition, scoped context, and parallel execution. However, the following dysfunctions must also be explicitly managed.

Dysfunction	Cause	Mitigation
Communication cost	Inter-agent message serialization, mutual re-summarization	Structured shared state, pass only essential fields on handoff
Consensus delay	Round repetition in Voting/Debate	Round cap, timebox, early termination condition
Token cost explosion	N agents × avg tokens × rounds	Conditional escalation, model tiering (Haiku → Sonnet → Opus)
Failure propagation	One agent failure halts entire chain	Circuit breaker, fallback agent, allow partial results
Observability complexity	Nested traces, difficult root cause analysis	Langfuse/OTel hierarchy, mandatory `agent_name` span tag

Decision Criteria

Introduce multi-agent only when subtasks benefit from (a) clearly separated roles or (b) independent parallel execution. For linear pipelines, single agent + tool-use is almost always cheaper.

2. Core Collaboration Patterns

This section organizes 6 patterns repeatedly observed in production. Most systems are hybrids combining 2-3 of these.

2.1 Orchestrator-Worker (Router Pattern)

The orchestrator agent receives user requests, decomposes them into sub-tasks, and assigns them to specialized worker agents. It collects worker results and synthesizes the final response.

Expand the diagram to read labels and follow connections.

Diagram

Representative Implementations: LangGraph Supervisor, Strands Agents Graph. In these, the orchestrator invokes workers and collects/synthesizes their results. OpenAI Agents SDK Handoff differs: it transfers control to the next agent (control transfer), so the orchestrator does not synthesize results afterward.
Best For: Clearly classifiable domains (SQL / code / search), fixed worker pools.
Caution: Orchestrator easily becomes bottleneck. Fan-out parallelizable sub-tasks with asyncio.gather etc.

2.2 Hierarchical Supervisor (Manager-Team)

Multi-layer expansion of Orchestrator-Worker. Top-level Supervisor delegates to multiple Team Leads, and each Team Lead manages Specialists.

Expand the diagram to read labels and follow connections.

Diagram

Representative Implementations: LangGraph Multi-Agent Supervisor, CrewAI Crew(process=Process.hierarchical, manager_agent=...).
Best For: Large-scale tasks requiring 3+ decision layers (e.g., full service refactoring, enterprise doc generation).
Caution: Deeper supervisor hierarchies increase latency and cost. Recommend not exceeding 2-3 levels.

2.3 Voting / Ensemble

Send the same question independently to N agents (or models) and conclude by majority vote or weighted average.

Techniques: Self-consistency, majority voting, weighted ensemble. Mixture-of-Agents (MoA) is not a vote but layered aggregation, where an aggregator LLM in a later layer synthesizes the outputs of the previous layer; treat it separately from voting.
Best For: Math problems, classification tasks, RAG answer verification with high hallucination risk.
Cost: Exactly N times. Many cases report Haiku × 5 ensemble achieving higher accuracy than Opus × 1.
Implementation Tip: Secure diversity by setting different temperatures per agent or using different prompt templates.

2.4 Debate / Adversarial

Two agents critique and rebut each other's answers through rounds, then a third judge agent selects the conclusion.

Expand the diagram to read labels and follow connections.

Diagram

Best For: Complex reasoning, code defect exploration, policy/ethical judgment.
Effect: Society of Minds and Multi-Agent Debate papers report accuracy improvements vs single agent.
Limitation: Token cost accumulates per round. Typically capped at 2-3 rounds + Judge structure.

2.5 Plan-and-Execute

Planner agent establishes overall execution plan first, then Executor agent(s) execute step-by-step. When needed, Re-Planner receives intermediate results and revises the plan.

Representative Implementations: LangChain Plan-and-Execute implements this pattern directly. OpenAI Deep Research and Claude Code's TodoWrite-based task list also appear to plan first and then execute step by step, but this is the author's interpretation of their observable behavior. The internal architecture of the two products (for example, a Planner/Browser/Writer role split or an executor setup) has no verified primary source (as of 2026-10-29).
Best For: Long-running tasks (research reports, large-scale refactoring, multi-step data pipelines).
Key Point: Secure cost efficiency by separating planning to high-performance reasoning models (for example, Opus-class or GPT-5-family models) and execution to mid/low-tier models. Pin and state the actual model IDs and endpoints at deployment.

2.6 Blackboard / Shared Memory

All agents record observations and intermediate results in central storage (blackboard). Each agent autonomously contributes when seeing tasks matching their expertise.

Storage: Redis, Postgres (LangGraph checkpointer), DynamoDB, or file system.
Best For: Long sessions (hours+), async collaboration, environments with frequent agent join/leave.
Caution: Race conditions — when multiple agents update the same field concurrently, serialize updates with an atomic compare-and-set (conditional write) gated on a version number. Optimistic locking, version numbers, and event sourcing are ways to implement that conditional write; listing them alone does not make the update atomic.

3. Implementation Framework Comparison (As of 2026-04)

Framework	Core Abstraction	Language	Key Patterns	License
LangGraph	StateGraph + Node	Python/JS	Supervisor, Swarm, Conditional routing	MIT
CrewAI	Crew + Agent + Task	Python	Sequential / Hierarchical / Consensus	MIT
AutoGen (v0.4+)	ConversableAgent + GroupChat	Python	Conversation + Workflow	MIT
Strands Agents SDK	Agent + Graph (Python)	Python	Orchestrator, Handoff	Apache 2.0
OpenAI Agents SDK	Agent + Handoff	Python/TS	Orchestrator-Worker, Handoff	Apache 2.0
Amazon Bedrock AgentCore	Runtime + Memory + Gateway + Identity	AWS SDK	Managed, MCP native	AWS managed

3.1 LangGraph

Defines state transitions between nodes as a graph based on StateGraph. Most patterns can be implemented in 10-50 lines with create_react_agent, create_supervisor, and swarm helpers. Deploy via LangGraph Platform (paid) or self-host, with Postgres/Redis checkpointer supporting long-running recovery.

3.2 CrewAI

Expresses role-based collaboration with natural language declarations. Provides 3 modes: Process.sequential, Process.hierarchical, Process.consensual, with manager_agent configuring hierarchical supervision structure. In production, clear "role / goal / backstory" writing determines quality.

3.3 AutoGen (v0.4+)

Microsoft Research framework redesigned in v0.4 based on actor-model. Configures conversational multi-agent with GroupChat, SelectorGroupChat, MagenticOne, with strong integration with code execution environments (Docker, Jupyter).

3.4 Strands Agents SDK

Open-source SDK released by AWS in 2025, tightly integrated with Bedrock but also supporting direct OpenAI/Anthropic calls. Agent + Graph abstraction similar to LangGraph, treats MCP tools as first-class citizens. Strands Agents Handoff has similar design intent as OpenAI Agents SDK handoff.

3.5 OpenAI Agents SDK

GA-level SDK released by OpenAI in March 2025 as Swarm successor. Can compose nearly all patterns with 4 primitives: Agent, Runner, handoff, guardrail. Fast observability setup with tracing integrated into OpenAI Dashboard.

3.6 Amazon Bedrock AgentCore

Amazon Bedrock AgentCore provides modular services — Runtime (session isolation, serverless scaling), Memory, Gateway, Identity, and Observability — for operating custom agents built with any framework (LangGraph, CrewAI, Strands, etc.) and foundation model in production. It is a different service from Agents for Amazon Bedrock, the managed product that composes agents declaratively via Action Groups and Knowledge Bases. MCP native support facilitates external tool integration, suitable for regulated industries with multi-agent execution within IAM/VPC boundaries.

Framework Selection Guide

Rapid Prototyping: CrewAI or OpenAI Agents SDK
Complex State Transitions & Recovery: LangGraph + Postgres checkpointer
AWS Ecosystem Lock-in: Strands Agents SDK or Bedrock AgentCore
Research & Experimentation: AutoGen v0.4 (easy exploration of various conversation patterns)

3.7 Minimal Implementation Example: Supervisor Pattern

# LangGraph — Supervisor routes to either Researcher or Coder
from typing import Literal, TypedDict
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-sonnet-4-5")
researcher = create_react_agent(llm, tools=[web_search], name="researcher")
coder = create_react_agent(llm, tools=[execute_python], name="coder")

class RouterState(TypedDict):
    input: str
    next: Literal["researcher", "coder"]

def supervisor(state: RouterState):
    decision = llm.invoke([
        {"role": "system", "content": "Route to 'researcher' or 'coder'. Reply with one word."},
        {"role": "user", "content": state["input"]},
    ]).content.strip().lower()
    # Constrain the free-form LLM output to the allowed labels; fall back on a mismatch
    route = decision if decision in ("researcher", "coder") else "researcher"
    return {"next": route}

graph = StateGraph(RouterState)
graph.add_node("supervisor", supervisor)
graph.add_node("researcher", researcher)
graph.add_node("coder", coder)
graph.set_entry_point("supervisor")
graph.add_conditional_edges("supervisor", lambda s: s["next"],
                             {"researcher": "researcher", "coder": "coder"})
graph.add_edge("researcher", END)
graph.add_edge("coder", END)
app = graph.compile()

# CrewAI — Hierarchical Crew
from crewai import Agent, Task, Crew, Process

manager = Agent(role="Engineering Manager",
                goal="Distribute, review, and approve",
                backstory="10-year platform lead")
coder = Agent(role="Coder", goal="Implement features", backstory="...")
reviewer = Agent(role="Reviewer", goal="Code review", backstory="...")

crew = Crew(agents=[coder, reviewer],
            tasks=[Task(description="Implement auth endpoint", agent=coder,
                        expected_output="Auth endpoint code that passes tests"),
                   Task(description="Review and feedback", agent=reviewer,
                        expected_output="Review comments with a list of requested changes")],
            manager_agent=manager,
            process=Process.hierarchical)
result = crew.kickoff()

# OpenAI Agents SDK — Handoff
from agents import Agent, Runner, handoff

triage = Agent(
    name="Triage",
    instructions="Classify questions and route to appropriate agent.",
    handoffs=[
        handoff(Agent(name="BillingBot", instructions="Billing inquiries")),
        handoff(Agent(name="TechBot", instructions="Technical inquiries")),
    ],
)
result = await Runner.run(triage, input="I was charged twice for this invoice.")

Model	Description	Representative Implementations
Shared Memory	Everyone reads/writes central storage	LangGraph `StateGraph`, Redis/Postgres
Message Passing	Agent communication via message queue only	AutoGen GroupChat, AWS SQS
Blackboard	Event sourcing + subscription	Kafka, EventBridge
Handoff Context	Context passed at call time; by default propagates full conversation history	OpenAI Agents SDK, Strands Handoff

In production, Shared Memory + Handoff Context combination is most common. Common state (user requests, accumulated results) in shared state, agent-specific work instructions in handoff payload. Note that OpenAI Agents SDK and Strands handoffs by default propagate the prior conversation history to the next agent, so you must set explicit input filters to scope the context.

4.2 Conflict Resolution Strategies

Voting: Majority vote or weighted average. Designate tiebreaker agent for ties.
Referee Agent: Judge makes final decision in Debate pattern.
Deterministic Winner Rule: Hard rules like "security reviewer rejection mandates rework."
Priority Queue: Other agents wait when urgent work is in progress.

4.3 Failure Recovery

Expand the diagram to read labels and follow connections.

Diagram

Retry Budget: Max retries and token budget per agent. Escalate upward when exceeded.
Per-Agent Circuit Breaker: Temporarily block specific agent when consecutive failure rate exceeds threshold.
Fallback Agent: Bypass with simpler prompt + cheaper model. Accept quality degradation to secure availability.
Checkpointer Recovery: Resume from intermediate state with LangGraph/Strands checkpoint feature (essential for long tasks).

5. Observability (Langfuse + OpenTelemetry)

Multi-agent system traces naturally have hierarchical structure. Correctly representing this in Langfuse/OTel dramatically simplifies debugging and performance analysis.

5.1 Trace Hierarchy Design

Trace: user-request-<uuid>
└── Span: orchestrator.plan
    ├── Span: worker.retriever
    │   ├── Span: tool.vector_search
    │   └── Span: llm.claude-opus-4-7
    ├── Span: worker.coder
    │   └── Span: llm.sonnet-4-5
    └── Span: judge.reviewer
        └── Span: llm.opus-4-7

5.2 Essential Span Attributes

Consistently assign the following attributes to each span. These form the basis for dashboard filtering and alerting in operations.

agent.name: Agent identifier (e.g., retriever, coder, judge)
agent.role: Role (e.g., worker, supervisor, critic)
agent.model: Model used (e.g., claude-opus-4-7, gpt-5-reasoning)
handoff.reason: Handoff reason (when switched to another agent)
handoff.from / handoff.to
round.index: Voting/Debate round number
tokens.input / tokens.output / cost.usd

5.3 Langfuse Dashboard Examples

Per-Agent Latency p95: agent.name Identify bottlenecks by grouping
Handoff Heatmap: handoff.from × handoff.to matrix
Per-Round Cost: Debate/Voting pattern cost convergence verification
Failure Rate: status=error spans by agent.nameAggregate

OpenTelemetry Integration

Langfuse v3.x operates as an OTel native collector. To connect as a single trace, instrument the app with the OpenTelemetry SDK, configure an OTLP exporter pointing at the Langfuse ingestion endpoint, and propagate the traceparent W3C context across service boundaries. Configured this way, it correlates by the same trace ID with other OTel-compatible backends such as AWS X-Ray, Datadog, and Grafana Tempo.

6. Cost & Latency Patterns

6.1 Cost Increase Factors

Agent Count N: Cost is linear in N only under the assumption of fixed calls and tokens per agent. Voting is exactly N times when each agent is called once.
Round Count R: Debate 2-3 rounds basis cost × 2~3 times.
Context Accumulation: Previous responses accumulate in context as rounds progress, so input token cost increases nonlinearly; the linear assumption above does not hold once per-round context grows.
Tool Call Chain: tool-use responses also enter LLM again, incurring cost.

6.2 Cost Optimization Checklist

Item	Technique
Model Tiering	Planner=Opus, Worker=Sonnet, Summarizer=Haiku
Prompt Caching	Claude prompt caching, OpenAI prompt cachingReuse system prompts with
Conditional Escalation	Start simple tasks with Haiku, call higher model if confidence low
Parallel Execution	Shorten latency with `asyncio.gather`/`Promise.all`for independent sub-tasks
Intermediate Result Summarization	Compress context by periodically summarizing long conversation history
Early Termination	Cancel remainder when majority secured in Voting

6.3 Latency Optimization

Streaming Handoff: Next agent starts processing from first token (speculative execution).
Fan-out then first-wins: N simultaneous requests to N agents, use first reliable response to arrive.
Prewarming: Keep frequently-used agents in idle state (session cache).

Because streaming handoff and fan-out start downstream processing before the response is complete, any side-effecting action (writes, external calls, resource changes) must be gated to run only after the arguments are fully received and validated. Otherwise an irreversible action may execute first on incomplete or invalid arguments.

7. AIDLC Stage-by-Stage Application

AIDLCThis section organizes how multi-agent patterns apply at each AIDLC stage.

7.1 Inception (Conception & Planning)

Planner Agent: Transform ambiguous requirements into structured backlog.
Critic Agent: Critically review planner outputs to identify gaps/contradictions.
Combination: Plan-and-Execute + Debate hybrid.

7.2 Construction (Development & Verification)

Coder Agent: Feature implementation (Sonnet).
Reviewer Agent: Code review (Opus, can separate security/quality).
Test Writer Agent: Test generation and execution.
Combination: Orchestrator-Worker, Hierarchical Supervisor.

7.3 Operations (AgenticOps)

Monitor Agent: Continuous observability data analysis (Haiku).
Diagnosis Agent: Root cause analysis when anomalies detected (Sonnet).
Responder Agent: Automatic recovery within predefined guardrails (Sonnet/Opus).
Human Escalation Agent: Route risky changes to approval queue.
Combination: Autonomous ResponseDetailed in Blackboard + Orchestrator-Worker.

Common Principles Across AIDLC

Map all agent outputs from all stages to Ontology for consistent vocabulary.
Inter-stage handoffs mediated by artifacts (PRD, ADR, RCA docs) — context not lost even when agent sessions end.

8. Case Study: AgenticOps Autonomous Response Pipeline

Example of applying multi-agent patterns to actual EKS incident response. Autonomous Response document implements the 3 stages (Detection → Judgment → Execution) with agents.

8.1 Architecture

Expand the diagram to read labels and follow connections.

Diagram

8.2 Roles and Model Tiering

Agent	Model	Avg Calls/Hour	Monthly Cost (Estimate)
Monitor	claude-haiku-4-5	3,600	~$20
Diagnosis	claude-sonnet-4-5	50	~$30
Responder	claude-sonnet-4-5 + Opus escalation	30	~$40
Judge (risky action approval)	claude-opus-4-7	5	~$15

The monthly costs in the table are the author's unvalidated estimates, not measured results. Call counts alone cannot reproduce the amounts; a comparison must compute them under the same assumptions for input/output tokens per call, duty cycle, prompt cache hit rate, per-token price by model, and the Opus escalation ratio. The direction holds that using an Opus-class model for a high-call role such as Monitor raises cost substantially, but "monthly $1,000+" and "95% cost reduction from tiering alone" are estimates that cannot be computed from this table.

8.3 Handoff Contract

The structure of the payload passed between agents is defined with JSON Schema; the block below is a concrete instance that conforms to that schema. Keep the schema (field types, required-ness) separate from the actual values passed.

{
  "incident_id": "inc-20260418-0042",
  "from_agent": "diagnosis",
  "to_agent": "responder",
  "severity": "P2",
  "root_cause": "Pod OOMKilled — memory limit 512Mi, actual peak 780Mi",
  "evidence": {
    "metrics": ["container_memory_working_set_bytes"],
    "logs": ["OOMKilled at 2026-04-18T10:32:15Z"],
    "affected_resources": ["deploy/api-gateway", "ns/prod"]
  },
  "proposed_action": {
    "type": "patch_deployment",
    "changes": {"spec.template.spec.containers[0].resources.limits.memory": "1Gi"}
  },
  "risk_level": "low",
  "requires_human_approval": false
}

This schema maps 1:1 to Incident, Resource, Action entities, ensuring vocabulary consistency between agents. Fields like requires_human_approval and risk_level, however, are self-asserted by the sending agent and must not be trusted as-is. Whether human approval is required, and the authorization to perform an action, must be decided by a policy at the receiving trust boundary (gateway/policy engine) rather than by a payload field, and that policy must also authorize the actual action (proposed_action).

8.4 Results (3 Months Operation)

The figures below are illustrative figures for the case (a hypothetical scenario, not measured data). To present them as real operational results, state the measurement window, the number of incidents in scope (the denominator), the criteria for autonomous resolution and false positives, and the measurement method.

MTTR: 112 min → 6 min (95% reduction)
Autonomous Resolution Rate: of all incidents 68%
Human Escalation: 32% (High risk + new patterns)
False positive: 3% or less (Guardrail + Judge combination effect)

9. Anti-patterns: Learning from Failures

9.1 God Agent

Pattern of cramming all tools and knowledge into one agent. Initially convenient, but tool-choice accuracy can drop as the number of tools grows. The figures in the symptoms below (15 or 20 tools, 2,000 tokens) are the author's workload-dependent hypotheses; confirm the actual thresholds with a workload benchmark.

Symptoms: System prompt exceeds 2,000 tokens, tools exceed 20.
Solution: Separate agents by domain + Orchestrator-Worker introduction.

9.2 Infinite Handoff Loop

A → B → A → B → ... Ping-pong. Each agent judges it is not their role and keeps passing.

Symptoms: Same handoff pair repeats 3+ times in same trace.
Solution: Set handoff hop limit, escalate when loop detected.

9.3 Voting Without Consensus

Failing to reach a majority happens not only with an even number of agents tying, but also with an odd number when all responses differ (no majority) or some agents abstain.

Solution: An odd configuration or a tiebreaker only resolves ties. To also handle no-majority and abstentions, define a default behavior — such as falling back to a judge agent or escalating to a human — independent of whether the agent count is odd or even.

9.4 Shared State Race Condition

Two agents simultaneously update same blackboard field, one side result lost.

Solution: Optimistic locking (version number), CRDT, or event sourcing.

9.5 Observability Gap

Agent internal LLM calls appear in trace but inter-agent handoffs separated into different traces, cause tracking impossible.

Solution: traceparent header propagation, all agents add spans to same root trace.

9.6 Prompt Injection Propagation

Injection contained in user input passed as-is from one agent to next and interpreted as system command.

Solution: Sanitize input at Gateway layer, pass inter-agent handoff payload only as user/tool role not system role. Role tagging alone, however, is not sufficient: the authority to call a given tool or access given data must be enforced by the gateway/runtime as a separate policy, not inferred from the role of the transferred message.

10. Operations Best Practices Checklist

Checklist items before putting multi-agent system into production.

11. References

Framework Official Documentation

LangGraph — StateGraph, Supervisor, Swarm tutorials
CrewAI — Sequential/Hierarchical process guide
AutoGen — v0.4 actor model, GroupChat
Strands Agents SDK — AWS open-source SDK
OpenAI Agents SDK — Handoff, Guardrails
Amazon Bedrock Agents — Action Group, Knowledge Base

Papers & Blogs

Anthropic, "Building effective agents" (2024) — pattern source
OpenAI, "Introducing Deep Research" (2025) — Plan-and-Execute case
OpenAI, "Swarm: experimental framework" (2024) — Agents SDK predecessor
Du et al., "Improving Factuality and Reasoning in Language Models through Multiagent Debate" (2023)
Wang et al., "Mixture-of-Agents Enhances Large Language Model Capabilities" (2024)

Autonomous Response — AgenticOps Agent-Driven pattern practical application
Observability Stack — Langfuse + OTel details
Ontology Engineering — common agent vocabulary

1. Why Multi-Agent?​

2. Core Collaboration Patterns​

2.1 Orchestrator-Worker (Router Pattern)​

2.2 Hierarchical Supervisor (Manager-Team)​

2.3 Voting / Ensemble​

2.4 Debate / Adversarial​

2.5 Plan-and-Execute​

2.6 Blackboard / Shared Memory​

3. Implementation Framework Comparison (As of 2026-04)​

3.1 LangGraph​

3.2 CrewAI​

3.3 AutoGen (v0.4+)​

3.4 Strands Agents SDK​

3.5 OpenAI Agents SDK​

3.6 Amazon Bedrock AgentCore​

3.7 Minimal Implementation Example: Supervisor Pattern​

4. State Sharing, Conflict Resolution & Failure Recovery​

4.1 State Sharing Models​

4.2 Conflict Resolution Strategies​

4.3 Failure Recovery​

5. Observability (Langfuse + OpenTelemetry)​

5.1 Trace Hierarchy Design​

5.2 Essential Span Attributes​

5.3 Langfuse Dashboard Examples​

6. Cost & Latency Patterns​

6.1 Cost Increase Factors​

6.2 Cost Optimization Checklist​

6.3 Latency Optimization​

7. AIDLC Stage-by-Stage Application​

7.1 Inception (Conception & Planning)​

7.2 Construction (Development & Verification)​

7.3 Operations (AgenticOps)​

8. Case Study: AgenticOps Autonomous Response Pipeline​

8.1 Architecture​

8.2 Roles and Model Tiering​

8.3 Handoff Contract​

8.4 Results (3 Months Operation)​

9. Anti-patterns: Learning from Failures​

9.1 God Agent​

9.2 Infinite Handoff Loop​

9.3 Voting Without Consensus​

9.4 Shared State Race Condition​

9.5 Observability Gap​

9.6 Prompt Injection Propagation​

10. Operations Best Practices Checklist​

11. References​

Framework Official Documentation​

Papers & Blogs​

Related Documentation​

1. Why Multi-Agent?

2. Core Collaboration Patterns

2.1 Orchestrator-Worker (Router Pattern)

2.2 Hierarchical Supervisor (Manager-Team)

2.3 Voting / Ensemble

2.4 Debate / Adversarial

2.5 Plan-and-Execute

2.6 Blackboard / Shared Memory

3. Implementation Framework Comparison (As of 2026-04)

3.1 LangGraph

3.2 CrewAI

3.3 AutoGen (v0.4+)

3.4 Strands Agents SDK

3.5 OpenAI Agents SDK

3.6 Amazon Bedrock AgentCore

3.7 Minimal Implementation Example: Supervisor Pattern

4. State Sharing, Conflict Resolution & Failure Recovery

4.1 State Sharing Models

4.2 Conflict Resolution Strategies

4.3 Failure Recovery

5. Observability (Langfuse + OpenTelemetry)

5.1 Trace Hierarchy Design

5.2 Essential Span Attributes

5.3 Langfuse Dashboard Examples

6. Cost & Latency Patterns

6.1 Cost Increase Factors

6.2 Cost Optimization Checklist

6.3 Latency Optimization

7. AIDLC Stage-by-Stage Application

7.1 Inception (Conception & Planning)

7.2 Construction (Development & Verification)

7.3 Operations (AgenticOps)

8. Case Study: AgenticOps Autonomous Response Pipeline

8.1 Architecture

8.2 Roles and Model Tiering

8.3 Handoff Contract

8.4 Results (3 Months Operation)

9. Anti-patterns: Learning from Failures

9.1 God Agent

9.2 Infinite Handoff Loop

9.3 Voting Without Consensus

9.4 Shared State Race Condition

9.5 Observability Gap

9.6 Prompt Injection Propagation

10. Operations Best Practices Checklist

11. References

Framework Official Documentation

Papers & Blogs

Related Documentation