Topic of the Day: Multi-Agent AI Systems

1. Hierarchical (Manager-Worker)

A top-level orchestrator decomposes the goal and delegates sub-tasks to worker agents. Workers report back, the orchestrator synthesises results. Best for: complex tasks with clear decomposition (e.g. multi-chapter research reports, end-to-end software features).

1. Single agent fi team of agents

Just as great companies hire specialists instead of one generalist, great AI systems use specialised

2. Orchestrator = the brain

One agent plans and delegates; others execute. The quality of the orchestrator determines the quality of

2. Sequential Pipeline

Agents hand off work in a fixed order -- like an assembly line. Agent A extracts data, Agent B cleans it, Agent C analyses it, Agent D writes the report. Best for: data transformation, ETL-style workflows.

3. Peer-to-Peer Collaboration

Agents communicate directly with each other based on capability, with no fixed hierarchy. Any agent can delegate to any other. Highly flexible but harder to debug. Best for: open-ended creative tasks, research

3. Plan-and-Execute = smart cost control

Use an expensive model to plan, cheap models to execute. 90% cost savings. This will be standard

4. Plan-and-Execute

A powerful (and expensive) frontier model creates a detailed plan. Cheap, fast models execute the individual steps. This pattern can reduce costs by up to 90% versus using a frontier model for every step. Best for: cost-sensitive production workloads. How Agents Communicate: Key Protocols in 2026 In 2026, three protocols dominate agent-to-agent communication: (cid:127) Agent2Agent (A2A) -- Google + Salesforce standard: Open protocol allowing agents from different vendors to hand off tasks, share context, and collaborate across platforms. Marks the shift from siloed agents to a true agentic internet. (cid:127) Model Context Protocol (MCP) -- Anthropic's tool layer: Standardises how agents connect to external tools (databases, APIs, file systems). Think of it as USB-C for AI tools -- one protocol,

(cid:127) LangGraph / AutoGen message passing: Framework-specific graph-based message routing, popular in enterprise deployments for fine-grained control over agent state machines. Why it matters for you: Understanding these protocols means you can design systems where Salesforce agents, custom Python agents, and cloud provider agents all work together seamlessly -- the foundation of every major enterprise AI transformation happening right now.

Google DeepMind AlphaCode 2: multi-agent system where one agent genSeurraptaesss ceodd 8e5 athn dp earncoetnhteilre c orift icqoumesp eatnitdiv ree pfinroegsr aitmmers Salesforce Agentforce: orchestrates sales, service, and marketingR aegdeunctes dfr aovme raa gsien ghlaen pdllaet ftoimrme ubsyi n3g4 %A2 inA ppirloott odceoplloyments Microsoft GitHub Copilot Workspace: planner + editor + tester agReenstso lcvoelsla ~b4o0ra%te o tfo a rsessigonlveed e insstiuree sG aituHtuobn oismsouuessl yend-to-end IBM WatsonX Orchestrate: routes tasks to the cheapest cap9a0b%le c mosotd reeld uuscitniogn a v ms.u ultsi-inagg eGnPt rTo-u4t eforr all tasks

4. Protocols are the glue

A2A + MCP are the emerging standards. Learn these like you learned REST APIs -- they will be

5. Governance is not optional

As agents act autonomously, 'governance agents' that audit others are becoming as important as the task

Now that you understand the fundamentals of multi-agent systems, here are the logical next topics in your

fi Memory systems for agents -- how agents retain context across long-running tasks (episodic,

fi Tool use & function calling -- how agents invoke APIs, browsers, and databases autonomously fi Agent evaluation & evals frameworks -- how to measure and improve agent reliability in production fi RAG (Retrieval-Augmented Generation) as agent memory -- connecting agents to your company's

fi Security & prompt injection attacks -- the biggest current vulnerability in deployed agent systems Agent Trends Report, Deloitte Tech Predictions 2026, MachineLearningMastery, IBM Think

What Is a Multi-Agent System?

A Multi-Agent System (MAS) is an architecture where multiple specialised AI agents collaborate to complete tasks that are too complex, too long, or too broad for a single model to handle alone. Think of it like a well-run team in a company -- instead of one person doing everything, you have a manager agent that orchestrates a group of specialist worker agents, each expert at a narrow skill. The analogy to microservices in software engineering is intentional. Just as monolithic applications were replaced by distributed services in the 2010s, monolithic single-prompt AI interactions are being replaced by collaborative agent networks in the 2020s. Key insight: Multi-agent research pipelines outperformed single-agent Claude Opus by 90.2% on complex benchmarks -- not because the individual agents were smarter, but because specialisation + parallel execution + cross-checking produced dramatically better outcomes.

Orchestrator Agent Breaks the goal into sub-tasks, assigns them, traPcrkosje pcrto Mgraensasger Specialist Agents Execute narrowly-scoped tasks (research, codinDg,o rmevaiienw E, xeptec.r)ts Memory Layer Shared short-term context + long-term vector DBShared Notes / Wiki Tool Layer APIs, browsers, code interpreters, databases Team's Toolbox

Structured messages passed between agents (JESmOaNil // AS2laAc k/ MCP)