TL;DR
AI agents are the hot new thing. Autonomous, LLM-powered systems that plan, reason, and collaborate. Multi-agent architectures are popping up everywhere, from AutoGPT to specialized agent teams. But here’s the thing: these „novel“ orchestration patterns look suspiciously familiar to anyone who’s spent time in distributed systems or classic software architecture. Sequential pipelines? That’s just Pipes and Filters. Concurrent agents voting on results? Welcome to MapReduce. Group chat coordination? Meet the Mediator pattern. This article breaks down common AI agent patterns, shows you their software engineering ancestors, and predicts what’s coming next. Spoiler: it’s all remix.
Introduction
You know what’s funny about the AI agent explosion? Everyone acts like we just invented coordination.
Multi-agent systems are everywhere now. You’ve got AutoGPT spawning agents like they’re going out of style. Microsoft’s pushing agent frameworks. Open-source projects are building agent orchestrators with names that sound like they came from a sci-fi novel. And the patterns have fancy new labels: sequential orchestration, concurrent orchestration, group chat orchestration, handoff orchestration, magentic orchestration.
Here’s what nobody’s saying out loud: we’ve been solving these exact problems for decades.
That sequential agent chain? It’s a Unix pipeline with LLMs instead of grep and awk. Those concurrent agents aggregating results? That’s literally MapReduce. The group chat manager coordinating everyone? It’s the Mediator pattern from the Gang of Four book that’s been sitting on your shelf since college.
Don’t get me wrong. AI agents are legitimately cool. They bring natural language understanding, reasoning, and adaptability to problems that used to need hard-coded logic. But the underlying coordination mechanics? Those are borrowed straight from distributed systems, design patterns, and algorithms we’ve known about since before most of us were born.
So let’s do something useful. Let’s map out the common AI agent patterns, trace their lineage to classic software engineering, and figure out what’s actually new versus what’s just a fresh coat of paint. Because if you understand the parallels, you can design better agent systems. And maybe, just maybe, we can predict what patterns are coming next.
The Basic Orchestration Patterns
Modern AI agent systems usually involve multiple specialized agents working together instead of one giant monolithic brain. This makes sense. It’s the same reason we break software into microservices or functions. You want each piece doing one thing well, then you coordinate them to solve complex problems[1][2][3].
Let’s walk through the fundamental patterns.
Sequential Orchestration: The Assembly Line
Sequential orchestration is dead simple. You line up your agents in a predetermined order, and each one’s output becomes the next one’s input. Agent A does its thing, passes the baton to Agent B, who passes it to Agent C, and so on[1][4].
Think of it like stages in an assembly line. Or if you’re old school, think Unix pipelines. One process outputs to stdout, the next reads from stdin, and you chain them together with pipes[4][5].
This pattern works great when your task has clear, successive stages that build on each other[1][4]. Let’s say you’re building a research assistant. Agent 1 interprets the user’s question. Agent 2 gathers relevant facts from a database. Agent 3 drafts an answer. Agent 4 reviews the draft for accuracy. Each step needs the previous step’s context, so a linear flow makes perfect sense.
graph LR
A[User Query] --> B[Agent 1:<br/>Interpret Question]
B --> C[Agent 2:<br/>Gather Facts]
C --> D[Agent 3:<br/>Draft Answer]
D --> E[Agent 4:<br/>Review Draft]
E --> F[Final Answer]
style A fill:#e1f5ff
style F fill:#c8e6c9
style B fill:#fff9c4
style C fill:#fff9c4
style D fill:#fff9c4
style E fill:#fff9c4The trade-off is latency. You’re waiting for each stage to finish before the next one starts. And if Agent 2 screws up, every agent downstream is working with garbage data[1][4]. You also lose flexibility. If you realize halfway through that you need to loop back or branch, tough luck. The pipeline is fixed.
But when you have a well-defined process that doesn’t need backtracking, sequential orchestration is clean, debuggable, and easy to reason about. Microsoft’s AI architecture guide explicitly calls this out as the Pipes and Filters pattern, just with AI agents instead of deterministic functions[4][5].
Concurrent Orchestration: Fan-Out, Fan-In
Concurrent orchestration flips the script. Instead of one agent after another, you run multiple agents in parallel on the same input[1][7][9].
A central coordinator dispatches the task to several agents at once. Each agent tackles it from a different angle or with different expertise. Then you aggregate their results into a final answer[1][9][10]. Maybe you vote on the best response. Maybe you combine them. Maybe you use an algorithm to reconcile conflicts.
This is your classic Fan-Out/Fan-In pattern from distributed computing[9][10]. Or MapReduce if you squint. You’re exploiting concurrency to get speed and diversity. Instead of one perspective, you get three, five, ten different takes on the same problem[7][8][9].
This pattern shines when you want ensemble reasoning or when multiple independent analyses improve quality. I’ve seen setups where one agent has a technical lens, another has a creative lens, and a third evaluates business impact. All three look at the same input simultaneously, then a fourth agent synthesizes their insights.
graph TD
A[User Input] --> B[Coordinator]
B --> C[Technical Agent]
B --> D[Creative Agent]
B --> E[Business Agent]
C --> F[Aggregator:<br/>Synthesize Results]
D --> F
E --> F
F --> G[Final Answer]
style A fill:#e1f5ff
style B fill:#ffe0b2
style C fill:#fff9c4
style D fill:#fff9c4
style E fill:#fff9c4
style F fill:#f8bbd0
style G fill:#c8e6c9The catch is result merging. If your agents disagree, you need logic to handle that. And if the task actually requires sequential steps or if agents would collide (like writing to the same file), concurrency causes more problems than it solves[1][3].
But for brainstorming, for getting diverse expert opinions, for speed? Concurrent orchestration is killer.
Group Chat Orchestration: The Panel Discussion
Group chat orchestration is where things get interesting. You set up multiple agents (and optionally humans) in a shared conversation thread[1]. A special chat manager mediates the discussion, deciding who speaks next and making sure everyone has access to the full chat history[1][12].
It’s like a panel of experts brainstorming in a chatroom. Or a code review discussion thread. Or a design meeting where people build on each other’s ideas[1][3].
The group chat manager is basically implementing the Mediator design pattern[11][12][13]. Instead of agents all talking to each other directly (which would be chaos), they communicate through one hub. The manager enforces turn-taking, keeps the conversation on track, and updates the shared context.
This pattern excels for problems that benefit from iteration, peer review, or debate[1][3]. One agent proposes a solution, another critiques it, a third checks for edge cases. You can even formalize this into a maker-checker loop: one agent generates content, another evaluates it, repeat until it’s good[1][47].
graph TD
A[User Input] --> M[Chat Manager<br/>Mediator]
M <--> B[Generator Agent]
M <--> C[Critic Agent]
M <--> D[Validator Agent]
M <--> H[Human<br/>Optional]
M --> E[Shared Chat<br/>History]
E --> F[Final Consensus]
style A fill:#e1f5ff
style M fill:#f8bbd0
style B fill:#fff9c4
style C fill:#fff9c4
style D fill:#fff9c4
style H fill:#e1bee7
style E fill:#b3e5fc
style F fill:#c8e6c9The transparency is a huge win. Everything’s in the chat log, so you can audit decisions or bring a human into the loop naturally[1][3]. A human can just join the chat and steer the conversation if the agents are going off the rails.
The downside is overhead. If you need a quick, linear process, a multi-agent chat is overkill. You also have to design carefully to avoid infinite conversation loops or deadlocks where agents just keep talking past each other[1][3]. Usually works best with three or fewer active participants to keep it manageable.
The Advanced Patterns
Once you’ve got the basics down, things get more sophisticated. These next patterns introduce hierarchy, dynamic routing, and on-the-fly planning.
Hierarchical Orchestration: The Org Chart
Hierarchical orchestration introduces a chain of command[1][14]. You’ve got a leader or supervisor agent at the top, coordinating subordinate agents below. It’s like a project manager delegating tasks to team members, or a master process spawning worker threads.
In practice, many complex agent systems use this by default. A top-level planner breaks a goal into subtasks and assigns them to specialist agents. Some of those specialists might themselves manage lower-level agents, creating a tree or directed graph of roles[1][14][15].
Frameworks like CrewAI explicitly model this with a „crew“ controller that manages a DAG of tasks[14][15]. LangGraph represents workflows as state machines with supervisor nodes directing execution[14][15][16].
The benefit is structured control. The supervisor has the whole picture, can optimize globally, implement quality control, and handle recovery if something fails[1][3][14]. It’s easier to enforce complex sequences with branching, loops, or parallel sub-steps when one entity orchestrates everything.
graph TD
A[User Goal] --> S[Supervisor Agent<br/>Planner]
S --> T1[Task 1]
S --> T2[Task 2]
S --> T3[Task 3]
T1 --> W1[Research Agent]
T1 --> W2[Analysis Agent]
T2 --> W3[Code Agent]
W3 --> W4[Test Agent]
T3 --> W5[Review Agent]
W2 --> R[Results]
W4 --> R
W5 --> R
R --> S
S --> F[Final Output]
style A fill:#e1f5ff
style S fill:#f8bbd0
style T1 fill:#ffe0b2
style T2 fill:#ffe0b2
style T3 fill:#ffe0b2
style W1 fill:#fff9c4
style W2 fill:#fff9c4
style W3 fill:#fff9c4
style W4 fill:#fff9c4
style W5 fill:#fff9c4
style R fill:#b3e5fc
style F fill:#c8e6c9The risk is creating a bottleneck. If your supervisor is a single point of failure or a performance chokepoint, your whole system suffers[3]. A fully decentralized approach (no hierarchy, agents negotiate peer-to-peer) is more robust but way harder to design[3][48].
Most real-world systems default to hierarchy because it maps naturally to how we think about workflows. A main function calling helper functions. A manager assigning work to a team. It’s familiar, and familiar means fewer bugs.
Handoff Orchestration: The Escalation Chain
Handoff orchestration gets more dynamic. Agents pass control from one to another based on runtime context, not a predefined sequence[1][17].
It’s a chain of responsibility. The task flows through agents until someone solves it, or it escalates to a human as a last resort[17][18][19]. Each agent has a domain of expertise and knows its limits. When it sees something outside its scope, it hands off to the appropriate specialist[1][3][17].
Think customer support. An AI support agent tries to answer your question. If it detects you’re asking about billing, it transfers you to the billing agent. If the billing agent realizes it’s actually a technical issue, it hands off to the tech support agent. And if nobody can help, a human operator picks it up[1][17][19].
This pattern is useful when the optimal sequence isn’t known upfront. The path emerges as the input is analyzed. It’s analogous to microservice choreography or event-driven architecture in traditional systems[32][33][34]. No central brain dictates everything. Each component does its job, then signals the next appropriate component.
The flexibility is great for specialization. At each step, the most qualified agent handles the task. But you have to prevent ping-pong handoffs or infinite loops[1][3]. You need strong protocols: structured data passed along, context preserved, clear termination conditions[3][17].
graph TD
A[Customer Query] --> G[General Support Agent]
G -->|Can Handle?| S1[Resolve & Close]
G -->|Billing Question| B[Billing Agent]
G -->|Technical Issue| T[Tech Support Agent]
B -->|Can Handle?| S2[Resolve & Close]
B -->|Complex Issue| T
T -->|Can Handle?| S3[Resolve & Close]
T -->|Out of Scope| H[Human Operator]
H --> S4[Resolve & Close]
style A fill:#e1f5ff
style G fill:#fff9c4
style B fill:#ffecb3
style T fill:#ffe0b2
style H fill:#e1bee7
style S1 fill:#c8e6c9
style S2 fill:#c8e6c9
style S3 fill:#c8e6c9
style S4 fill:#c8e6c9When it works, handoff orchestration feels natural. The task finds its way to the right expert, like escalation tiers in a call center.
Magentic Orchestration: The Self-Organizing Team
Magentic orchestration is the most ambitious pattern. The term comes from Microsoft’s Semantic Kernel team and represents an „agentic“ approach where the workflow builds itself as agents collaborate[20][21].
Instead of a predefined plan, a manager agent (or planner) takes the user’s goal and incrementally develops a plan of action[20][21]. It creates a task list on the fly, queries specialist agents for information or proposals, adjusts the plan based on their feedback, and loops until the problem is solved[1][20][21].
It’s planning and execution happening simultaneously. The system figures out the approach while solving the problem, documenting steps as it goes[20][21].
Picture an incident response agent dealing with an unexpected server outage. No fixed playbook. The manager formulates a plan on the fly: query the diagnostics agent, consult the infrastructure agent, maybe bring in the rollback agent if needed. The plan evolves with each insight[1][20]. When the manager decides the goal is achieved, it either executes the final plan or presents it to a human for verification[20][21].
This is basically an autonomous project manager[20][21][22]. It’s related to the AutoGPT style of spawning tasks and agents recursively. The benefit is extreme flexibility and adaptability to unknown problems[22][23]. The system can even explain its reasoning, producing a transparent problem-solving trace (a „task ledger“) that’s invaluable for auditing[20][21].
graph TD
A[User Goal] --> M[Manager Agent<br/>Planner]
M --> P[Create Initial Plan]
P --> L[Task Ledger]
L --> Q1{Query Specialists}
Q1 --> D[Diagnostics Agent]
Q1 --> I[Infrastructure Agent]
Q1 --> R[Rollback Agent]
D --> F1[Feedback]
I --> F1
R --> F1
F1 --> U[Update Plan]
U --> L
L --> C{Goal Achieved?}
C -->|No| Q1
C -->|Yes| V[Verify Plan]
V --> E[Execute or Present<br/>to Human]
E --> O[Output]
style A fill:#e1f5ff
style M fill:#f8bbd0
style P fill:#ffe0b2
style L fill:#b3e5fc
style Q1 fill:#fff9c4
style D fill:#ffecb3
style I fill:#ffecb3
style R fill:#ffecb3
style F1 fill:#ffe0b2
style U fill:#ffe0b2
style C fill:#fff9c4
style V fill:#ffe0b2
style E fill:#e1bee7
style O fill:#c8e6c9The downside is resource intensity. All that planning and back-and-forth debate costs tokens and time. It’s not ideal for simple or time-sensitive tasks[20][21]. Use magentic orchestration when you truly have a complex, open-ended goal with no clear recipe, and you need the AI to brainstorm a solution path before executing it.
Wait, We’ve Done This Before
If you’ve been in software engineering for a while, you’re probably nodding along. These patterns aren’t new. They’re remixes of concepts we’ve had for decades.
AI agents may be new tech, but they’re extending traditional design patterns, not inventing from scratch. Let’s make the connections explicit.
Pipes and Filters: Sequential Agents
A sequential agent chain is literally the pipes-and-filters architectural pattern. Unix pipelines. Enterprise integration patterns. Each agent is a filter processing data and passing it to the next stage[4][5][6].
Same benefits (deterministic, ordered flow). Same drawbacks (latency, brittle to early failures). The only difference is that each stage is an LLM call instead of a shell command.
MapReduce: Concurrent Agents
Running agents in parallel and aggregating results? That’s MapReduce[7][8]. Each agent is a mapper working on the same input. The orchestrator is the reducer combining outputs.
It’s also Fan-Out/Fan-In from distributed computing[9][10]. Spawn multiple workers, join their results. We’ve been doing this since the dawn of parallel programming.
Consensus Protocols: Group Agreement
When multiple agents collaborate or vote on an outcome, you’re dealing with distributed consensus. Total order broadcast ensures all nodes in a distributed system see the same messages in the same order[29]. A group chat manager enforcing a single conversation thread is essentially achieving consensus through a leader algorithm.
If agents need to agree on a result or shared state, you’re solving the same problem as Paxos, Raft, or any other consensus protocol[26][27][28]. Just with natural language instead of network packets.
Mediator Pattern: Group Chat
The group chat orchestration is a textbook Mediator[11][12][13]. A central object coordinates communication among colleagues to avoid direct peer-to-peer chatter.
Same pattern, same benefits (centralized control, consistent state). Same drawbacks (single point of coordination). GUI frameworks have been using mediators for decades. Now we’re using them for agents.
Orchestration vs. Choreography: Microservices Déjà Vu
The orchestration vs. choreography debate from microservices architecture is back[32][33][34].
Orchestrated flows (sequential, group chat, hierarchical) have a central controller. Clear workflows, easy to observe, but central dependency.
Choreographed flows (handoff) rely on distributed decision-making. More flexible and scalable, but harder to trace and control.
The trade-offs are identical to microservices. Centralized clarity versus decentralized autonomy. We’re just applying them to agents now.
Chain of Responsibility: Handoff
The handoff pattern is the Chain of Responsibility design pattern with AI agents[17][18][19]. Pass a request along a chain of handlers until one handles it.
Same philosophy. Same dynamic routing. The only twist is that agents use natural language understanding to decide if they can handle the task or should delegate.
Two-Phase Commit: Plan Consensus
Coordinating multiple agents to work on a shared goal is like transaction coordination. Two-phase commit ensures distributed systems either all commit or all abort[24][25].
An AI planner consulting agents before finalizing a plan? That’s the prepare phase („Can you do this?“) followed by the commit phase („Okay, everyone execute“). If any agent vetoes during planning, the plan revises or cancels, just like 2PC aborting on a participant’s vote.
Distributed Reliability: Same Problems, Same Solutions
Multi-agent systems face the same reliability issues as any distributed system. Timeouts, partial failures, race conditions, network errors (okay, API errors, but same idea).
The mitigations are identical: retry policies, graceful degradation, circuit breakers to isolate failing agents[30][31]. We’re applying decades of distributed systems wisdom to AI agents.
In summary, these agent patterns aren’t revolutionary. They’re evolutionary. They take well-known coordination strategies and apply them to LLM-powered components. If you know your design patterns and distributed systems, you already know how to architect multi-agent AI.
What’s Coming Next
The patterns we have now are just the beginning. As agent ecosystems mature, we’ll see new variations emerge or existing ones hybridize. Here’s my prediction of what’s on the horizon.
Reflective QA Loops: Built-In Critics
Quality control loops are becoming first-class patterns. We already see critic or evaluator agents paired with generator agents in maker-checker setups[1][47]. The generator creates, the critic reviews, repeat until it’s good.
Expect this to get more elaborate. Multiple checkers. Adversarial red team agents stress-testing solutions. Specialized validators ensuring safety, accuracy, compliance.
It’s like code having linters, unit tests, and code reviewers. A pattern of AI self-audit before finalizing outputs.
Debate and Voting Ensembles
Building on concurrent patterns, we’ll formalize multi-agent debates. Two agents argue opposite viewpoints, a judge agent decides the winner[41][42]. Or five agents each propose solutions, they vote, and the majority wins.
This Socratic approach harnesses constructive disagreement. Research shows agents critiquing each other reduces reasoning errors[42][43][44]. Expect plug-and-play „N agents enter, one answer leaves“ patterns for improving reliability through consensus.
Market-Based Task Allocation
In systems with many tasks and agents, auction or contract-net protocols could emerge[45][46]. One agent announces a task, others bid if they can handle it, the manager awards it to the best bid.
This decentralized allocation ensures tasks go to the most qualified or available agent dynamically. It’s from classic multi-agent research but aligns perfectly with maximizing efficiency in large agent ecosystems.
Blackboard Architecture: Shared Workspace
The blackboard pattern is where agents cooperate via a shared memory or workspace[35][36][37]. Instead of passing messages, agents post partial results to a shared „blackboard“ that everyone can read and contribute to.
This was used in expert systems decades ago. Each agent triggers when it sees something relevant on the board. For LLMs, this could be a shared document or context buffer that agents take turns modifying.
It solves context-sharing in teams and enables asynchronous collaboration, like developers working on a shared git repo.
Swarm Patterns: Emergent Intelligence
Moving beyond a few coordinated agents, future systems might deploy swarms of simple agents. Dozens of micro-agents each handle a tiny part (analyze one paragraph each, explore solution variations), then aggregate.
The pattern is emergent consensus. No fixed hierarchy. Agents follow simple local rules (mimic the majority, reinforce promising answers) to converge on solutions. It’s swarm intelligence from robotics applied to LLMs[38][39][40].
Challenges include ensuring coherence, but techniques from ensemble learning and evolutionary algorithms could make this viable with lightweight model instances.
Human-AI Hybrid Workflows
We’ll formalize patterns that explicitly integrate human oversight. Handoff-to-human is already implicit in support scenarios[1][17], but expect clearer definitions.
An approval-required agent that always defers critical decisions to a person. A human-as-manager pattern where a person guides AI agents like a team lead. Centaur teams of humans and AIs working together.
Defining where the human fits (supervisor, fallback, tiebreaker, one of the voters) will be crucial for accountability in autonomous systems.
These emerging patterns show AI agents are following the same path as software components. We’ll likely see a library of agent patterns solidify as best practices, like the Gang of Four patterns for object-oriented design[11]. The art will be choosing the right pattern (or mix) for each problem[1][3].
Conclusion
So here’s the deal: AI agents are cool, but they’re not magic.
The orchestration patterns we’re seeing (sequential, concurrent, group chat, hierarchical, handoff, magentic) are evolutionary, not revolutionary. They borrow heavily from Pipes and Filters, MapReduce, Mediator, Chain of Responsibility, consensus protocols, and pretty much every distributed systems pattern you learned in school or on the job.
What’s actually new is applying natural language understanding and reasoning to these coordination problems. Instead of hard-coded routing logic, you’ve got agents that can interpret context and decide dynamically. Instead of fixed data transformations, you’ve got LLMs generating nuanced outputs. That’s legitimately powerful.
But the underlying mechanics? Those are decades old. And that’s a good thing. It means we have a playbook. We know what works, what doesn’t, and what trade-offs to expect. If you understand design patterns and distributed systems, you already have the mental models to design robust multi-agent AI systems.
The intersection of AI agents and software engineering is a rich field of reiteration. What’s old is new again, just with better hardware and smarter components. By recognizing these parallels, we can bridge the gap between AI researchers and software engineers. We can build systems that are as principled under the hood as they are novel on the surface.
Moving forward, expect AI agents to continue borrowing from and enriching software architecture. The patterns will evolve, hybridize, and expand. We’ll see reflective QA loops, debate ensembles, market-based allocation, blackboard architectures, swarm intelligence, and human-AI hybrid workflows join the roster.
But at the core, it’s still coordination. It’s still managing complexity through abstraction, modularity, and proven patterns. The technology may be cutting-edge, but the wisdom guiding it comes from decades of hard-earned engineering experience.
So the next time someone shows you their revolutionary new AI agent system, look under the hood. I bet you’ll find an old friend: a pipeline, a fan-out, a mediator, a chain of responsibility. And you’ll realize you already know how to make it better.
The future of AI agents isn’t about inventing new patterns from scratch. It’s about remixing the classics with intelligence. And that’s something software engineers have been doing since day one.
References
Multi-Agent Systems & Orchestration
[1] AI Agent Orchestration Patterns – Azure Architecture Center | Microsoft Learn https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
[2] Design multi-agent orchestration with reasoning using Amazon Bedrock | AWS Machine Learning Blog https://aws.amazon.com/blogs/machine-learning/design-multi-agent-orchestration-with-reasoning-using-amazon-bedrock-and-open-source-frameworks/
[3] Best Practices for Multi-Agent Orchestration and Reliable Handoffs | Skywork AI https://skywork.ai/blog/ai-agent-orchestration-best-practices-handoffs/
Sequential Orchestration & Pipes and Filters
[4] Pipes and Filters pattern – Azure Architecture Center | Microsoft Learn https://learn.microsoft.com/en-us/azure/architecture/patterns/pipes-and-filters
[5] Pipes and Filters – Enterprise Integration Patterns https://www.enterpriseintegrationpatterns.com/patterns/messaging/PipesAndFilters.html
[6] Pipe and Filter Architecture – System Design | GeeksforGeeks https://www.geeksforgeeks.org/system-design/pipe-and-filter-architecture-system-design/
Concurrent Orchestration, MapReduce & Fan-Out/Fan-In
[7] MapReduce – Wikipedia https://en.wikipedia.org/wiki/MapReduce
[8] MapReduce Patterns, Algorithms, and Use Cases | Highly Scalable Blog https://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/
[9] Fan-In and Fan-Out Patterns in Cloud and Distributed Systems | Medium https://medium.com/@minimaldevops/fan-in-and-fan-out-patterns-in-cloud-and-distributed-systems-0544235b9d6b
[10] Fan-out (software) – Wikipedia https://en.wikipedia.org/wiki/Fan-out_(software)
Group Chat Orchestration & Mediator Pattern
[11] Design Patterns: Elements of Reusable Object-Oriented Software | Gamma, Helm, Johnson, Vlissides (1994) https://en.wikipedia.org/wiki/Design_Patterns
[12] Mediator Design Pattern | Gang of Four https://www.geeksforgeeks.org/system-design/mediator-design-pattern/
[13] Mediator Pattern | Refactoring.Guru https://refactoring.guru/design-patterns/mediator (implied from search results)
Hierarchical Orchestration
[14] Mastering AI Agent Orchestration: Comparing CrewAI, LangGraph, and OpenAI Swarm | Medium https://medium.com/@arulprasathpackirisamy/mastering-ai-agent-orchestration-comparing-crewai-langgraph-and-openai-swarm-8164739555ff
[15] LangGraph vs CrewAI: Let’s Learn About the Differences | ZenML Blog https://www.zenml.io/blog/langgraph-vs-crewai
[16] Choosing the Right AI Agent Framework: LangGraph vs CrewAI vs OpenAI Swarm | nuvi Blog https://www.nuvi.dev/blog/ai-agent-framework-comparison-langgraph-crewai-openai-swarm
Handoff Orchestration & Chain of Responsibility
[17] Chain-of-responsibility pattern – Wikipedia https://en.wikipedia.org/wiki/Chain-of-responsibility_pattern
[18] Chain of Responsibility | Refactoring.Guru https://refactoring.guru/design-patterns/chain-of-responsibility
[19] Chain of Responsibility Design Pattern | GeeksforGeeks https://www.geeksforgeeks.org/system-design/chain-responsibility-design-pattern/
Magentic Orchestration & AutoGPT
[20] Semantic Kernel Agent Orchestration | Microsoft Learn https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-orchestration/
[21] Semantic Kernel: Multi-agent Orchestration | Microsoft DevBlogs https://devblogs.microsoft.com/semantic-kernel/semantic-kernel-multi-agent-orchestration/
[22] AI Agents: AutoGPT architecture & breakdown | Medium https://medium.com/@georgesung/ai-agents-autogpt-architecture-breakdown-ba37d60db944
[23] AutoGPT Guide: Creating And Deploying Autonomous AI Agents Locally | DataCamp https://www.datacamp.com/tutorial/autogpt-guide
Distributed Systems Patterns
[24] Two-Phase Commit | Martin Fowler https://martinfowler.com/articles/patterns-of-distributed-systems/two-phase-commit.html
[25] Two-phase commit protocol – Wikipedia https://en.wikipedia.org/wiki/Two-phase_commit_protocol
[26] Raft and Paxos: Consensus Algorithms for Distributed Systems | Medium https://medium.com/@mani.saksham12/raft-and-paxos-consensus-algorithms-for-distributed-systems-138cd7c2d35a
[27] Paxos vs. Raft: Have we reached consensus on distributed consensus? | arXiv https://arxiv.org/abs/2004.05074
[28] Raft Consensus Algorithm https://raft.github.io/
[29] Atomic broadcast – Wikipedia https://en.wikipedia.org/wiki/Atomic_broadcast
[30] Circuit Breaker Pattern – Azure Architecture Center | Microsoft Learn https://learn.microsoft.com/en-us/azure/architecture/patterns/circuit-breaker
[31] Circuit Breaker Pattern in Microservices | GeeksforGeeks https://www.geeksforgeeks.org/system-design/what-is-circuit-breaker-pattern-in-microservices/
Orchestration vs. Choreography
[32] Orchestration vs. Choreography in Microservices | GeeksforGeeks https://www.geeksforgeeks.org/system-design/orchestration-vs-choreography/
[33] Orchestration vs Choreography | Camunda https://camunda.com/blog/2023/02/orchestration-vs-choreography/
[34] Saga Orchestration vs Choreography | Temporal https://temporal.io/blog/to-choreograph-or-orchestrate-your-saga-that-is-the-question
Emerging Patterns
[35] Blackboard system – Wikipedia https://en.wikipedia.org/wiki/Blackboard_system
[36] Blackboard Architecture | GeeksforGeeks https://www.geeksforgeeks.org/system-design/blackboard-architecture/
[37] The Resurgence of Blackboard Systems | Medium https://medium.com/@shawncutter/the-resurgence-of-blackboard-systems-b10ea72a8326
[38] Swarm Intelligence: The Power of the Collective | FasterCapital https://fastercapital.com/content/Swarm-Intelligence–The-Power-of-the-Collective–Swarm-Intelligence-in-AI.html
[39] Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence | arXiv https://arxiv.org/abs/2503.03800
[40] Enterprise Swarm Intelligence: Building Resilient Multi-Agent AI Systems | AWS Community https://community.aws/content/2z6EP3GKsOBO7cuo8i1WdbriRDt/enterprise-swarm-intelligence-building-resilient-multi-agent-ai-systems
[41] Patterns for Democratic Multi-Agent AI: Debate-Based Consensus | Medium https://medium.com/@edoardo.schepis/patterns-for-democratic-multi-agent-ai-debate-based-consensus-part-1-8ef80557ff8a
[42] Voting or Consensus? Decision-Making in Multi-Agent Debate | arXiv https://arxiv.org/abs/2502.19130
[43] More Agents Is All You Need | arXiv https://arxiv.org/html/2402.05120v1
[44] Minimizing Hallucinations and Communication Costs: Adversarial Debate and Voting Mechanisms in LLM-Based Multi-Agents | MDPI https://www.mdpi.com/2076-3417/15/7/3676
[45] Contract Net Protocol – Wikipedia https://en.wikipedia.org/wiki/Contract_Net_Protocol
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Additional Resources
[47] Implementation of Maker and Checker (4-eyes) Principle | LinkedIn https://www.linkedin.com/pulse/implementation-maker-checker-4-eyes-principle-ajendra-singh
[48] When One AI Agent Isn’t Enough: Building Multi-Agent Systems | Medium https://medium.com/@nirdiamant21/when-one-ai-agent-isnt-enough-building-multi-agent-systems-755479f2c64d