book

Design Multi-Agent AI Systems Using MCP and A2A

Name: Design Multi-Agent AI Systems Using MCP and A2A
Author: Gigi Sayfan
ISBN: 9781806116478

by Gigi Sayfan

February 2026

Intermediate to advanced

536 pages

12h 16m

English

Packt Publishing

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Preface
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Part 1: Foundations of Agentic AI
Chapter 1: Introduction to Generative AI and AI Agents
Free benefits with your bookThe evolution of generative AIThe early days of symbolic AIThe expert systems eraThe rise of machine learning and statistical methodsDeep learning and the neural network renaissanceThe emergence of generative models, transformers, and language modelsThe rise of AI agentsIntroducing AI agentsAI agent characteristicsKey differences: chatbots versus agentsUse cases for agentic AIIntroducing the components of agentic AI systemsCommon components of agentic AI systemsTypes of agentic AI architecturesSingle-agent loopPlanner and executorsMulti-agent systemGraph-based architectureSummary
Chapter 2: Understanding How AI Agents Work
Understanding the agent loop: sense, think, actThe session workflowThe agent loop phasesWorkings of an agent loop in AutoGenThe core implementation of AutoGenManaging long conversations with memory, goals, and stateThe context windowWhy do LLMs have no memory?Memory in AutoGenPlanning and reasoning mechanismsWorking with reasoning modelsSingle-agent planning and reasoningMulti-agent planning and reasoningTool use and environment interactionsHow are user-defined tools implemented?How are tools declared?How does the LLM decide which tool to use?Tool use in AutoGenAgent evaluation and feedback loopsHuman feedbackAutomated evaluationIndustry benchmarksSummary
Chapter 3: A Hands-On Walk-Through of a Simple AI Agent
Technical requirementsIntroducing the k8s-ai agentSetting up a local kind clusterRunning the k8s-ai agentChecking the cluster with k8s-aiUnderstanding the k8s-ai agentic systemCode walk-through of the k8s-ai agentDefining and registering tools with OpenAIImplementing the tool-calling execution loopRunning and interacting with the Kubernetes chat agentSummary
Part 2: Building Your Own Agentic AI Framework
Chapter 4: Building a Tool-Based Agentic AI Framework
Technical requirementsAI-6 framework backend architecture and componentsCore engineInitializationRunning the engineExecuting the agentic loopThe LLM provider abstractionThe LLMProvider interfaceThe supported providersTool system architectureCustom tool supportWhat about built-in tools?Tool security and permissionsMemory managementWhat is a session?Short-term memory in the agentic loopSessions and conversationsLong-term memory and session loadingHandling long contextsSummary
Chapter 5: Implementing Custom Tools
Technical requirementsDefining provider-agnostic toolsOpenAI Python tool specificationOllama Python tool specificationDefining the AI-6 tool specification schemaDefining AI-6 custom toolsGeneral command-line toolsFilesystem toolsImplementing the echo toolTest runner toolBootstrap toolMemory toolsListing sessionsLoading sessionsGetting the current session IDDeleting sessionsImplementing a new tool from scratch with AI-6How to interact with AI-6Building a new GitHub tool with AI-6Implementing a new tool manuallySummary
Chapter 6: Creating Chat Interfaces Using Slack and Chainlit
Technical requirementsWhy the UI matters in agentic AI workflowsAgent feedback loops and visibilityInterruptibility and human-in-the-loop designDiagnosing and debugging agent behaviorsBalancing autonomy and user controlUI as a trust-building mechanismBuilding a Slack bot interfaceTaking the AI-6 Slack bot for a spinAuthentication and configuration setup**/.env.*Slack Bot initializationMessage handling and event processingChannel management and multi-user supportIntegration with the AI-6 engineDeveloping a web UI with ChainlitChainlit framework overviewTaking AI-6 Chainlit for a spinConfiguration and engine integrationApplication life cycle and event handlersThe on_chat_start event handlerThe on_message event handlerThe on_settings event handlerBest practices for interactive agent interfacesClarify the agent’s role and capabilitiesSafeguards – confirmations, dry-runs, and safety netsDesign for turn-based transparencyPrioritize tool invocation and feedback loopsEmbrace configurability and runtime controlLogs, metrics, and distributed tracing for agentic systemsSummary
Chapter 7: Integrating with the Model Context Protocol Ecosystem
Technical requirementsWhat is MCP?The motivation behind MCPMCP architecture core and core componentsMCP serversMCP clientsMCP hostsLocal versus remote MCP serversBenefits of using MCPThe MCP ecosystemBuilding MCP serversFilesystem MCP serverGitHub MCP serverBuilding MCP clientsThe MCPClient classConnecting to an MCP serverInvoking an MCP toolIntegrating MCP into the AI-6 frameworkThe universal MCPTool classMCP tool discoveryThe tool managerHow the engine uses the tool managerPutting it all togetherSummary

Part 3: Constructing Multi-Agent Systems
Chapter 8: Designing Multi-Agent Systems
Technical requirementsWhat is multi-agent orchestration and why does it matter?What is an AI agent?Adding explicit agents to AI-6Renaming Engine to AgentAdding a system prompt to the Agent configurationAdding enabled_tools and disabled_tools fields to the Agent configurationAdding a new AgentTool that wraps an AI-6 Agent instanceWhy do we need multi-agent systems?Context window limitationsSpecialization challengesModel selection constraintsCognitive load distributionFault tolerance and resilienceTestability and debuggingHow to orchestrate multiple agentsSequential orchestrationParallel orchestrationHierarchical orchestrationEvent-driven orchestrationCollaborative orchestrationHybrid orchestrationDesign considerationsIntroduction to the A2A protocolCore components of the A2A protocolA2A communication mechanismsAdding A2A support to AI-6Integrating A2A client capabilities into AI-6The A2ATool classConfiguration and setupDesigning collaborative agent workflowsAgent decomposition strategiesDecomposition by domain expertiseDecomposition by workflow stageDecomposition by capability patternDecomposition decision frameworkSystem prompt design patternsThe specialist agent templateThe coordinator agent templateThe validator agent templateDomain-specific prompt examplesTool assignment decision frameworkContext engineering for multi-agent systemsTypes of context in multi-agent systemsContext management strategiesContext window managementInter-agent context sharingTaking AI-6 and A2A for a rideMulti-agent patterns in actionConflict resolution and role assignment in multi-agent systemsBest practices for conflict preventionDesign-time conflict analysisRuntime conflict detectionClear escalation pathsSummary
Chapter 9: Implementing Multi-Agent Systems with A2A
Technical requirementsBuilding an AI-driven DevOps teamWhat makes DevOps suitable for multi-agent AI?MAKDO – a multi-agent Kubernetes DevOps systemSystem architectureThe coordinator agent – orchestrating operationsAgent interaction patternsLLM-driven agent coordinationExtending AI-6 with new toolsTool discovery and assignmentContext sharing and state managementDefining roles and responsibilities for specialized agentsFrom DevOps workflows to agent architectureWhy four agents instead of one?Why separate the analyzer and fixer?Why separate Slack_Bot?Tool access boundariesExtending the architectureKey design patternsImplementing the agentsConfiguration-driven agent implementationTool discovery and assignmentSession isolationConfiguration over codeKey takeawaysOrchestrating the team with a manager agentDynamic workflow executionDecision points and branchingHandling complex multi-step operationsMulti-cluster coordinationConditional remediationRetry and verification loopsError handling and recoveryAgent communication failuresPartial failuresCascading failuresState management and coordinationMemory tools for long-term stateAvoiding duplicate operationsProactive versus reactive orchestrationReactive mode – user-initiated operationsProactive mode – autonomous monitoringHybrid approach – event-driven coordinationIntegrating human feedback and control channelsThe human-AI interface challengeThe Slack_Bot agent architectureBuilding a custom MCP server with FastMCPThe FastMCP Slack serverCommunication patternsNotification hierarchyMessage formattingContextual messagingEscalation pattern for restricted operationsAlternative communication channelsSecurity and access controlObservability and audit trailMAKDO in action – a complete demo walk-throughDemo setupCreating cluster problemsStep 1 – demo start notificationStep 2 – analysis phaseStep 3 – remediation phaseStep 4 – demo completeKey observationsSummary
Chapter 10: Testing, Debugging, and Troubleshooting Multi-Agent Systems
Common failure modes in agentic systemsAgent hallucinationWrong tool use and tool misusePoor instruction followingInfinite loops and runaway agentsCommunication and coordination breakdownsTimeout and latency issuesState management and consistency errorsSecurity and permission failuresInstrumentation and observability for AI agentsCore observability primitivesLLM call tracingTool execution logsAgent state snapshotsSpan and trace hierarchiesMetadata enrichmentStructured logging and trace formatsLLM-specific observability toolsMetrics and KPIs for agent performanceReal-time monitoring and alertingDebugging with observability dataPrivacy and security considerationsDebugging tool use and function call chainsUnderstanding the tool call life cycleTracing function call chainsCommon tool use anti-patternsDebugging strategies for tool useBuilding tool use guardrailsDiagnosing coordination failures in multi-agent workflowsUnderstanding coordination failure patternsAgent interaction patterns and their failure modesTracing dependencies and data flowManaging shared state and detecting deadlocksState synchronization challengesDetecting and resolving deadlocksBuilding coordination guardrailsDesigning for resilience and recoverabilityRedundancy and failover mechanismsGraceful degradation strategiesRetry logic and circuit breakersCheckpointing and state recoveryHuman escalation patternsSummary
Chapter 11: Deploying Multi-Agent Systems
Technical requirementsSetting up dual KinD clusters for multi-agent deploymentInstalling KinD and prerequisitesCreating the control cluster configurationCreating the worker cluster configurationSetting up cluster networking and port forwardingVerifying cross-cluster connectivityDeploying a test workload to the worker clusterDeploying MAKDO to the control clusterPreparing the MAKDO configurationCreating Kubernetes secrets for API keysCreating the kubeconfig ConfigMapDeploying MAKDO as a Kubernetes deploymentBuilding and loading the MAKDO imageConfiguring MAKDO to connect to the worker clusterVerifying MAKDO deployment and logsDeploying k8s-ai to the worker clusterBuilding the k8s-ai Docker imageCreating k8s-ai deployment manifestsDeploying k8s-ai to the worker clusterExposing the k8s-ai service for cross-cluster accessVerifying k8s-ai deploymentCheck pod statusVerify pod healthCheck server logsTest the A2A endpointVerify service configurationTesting the k8s-ai Admin API and session managementExposing the Admin API portCreating a sessionListing active sessionsDeleting a sessionUsing sessions with the A2A protocolEnabling secure communication between clustersService discovery and networkingHow MAKDO discovers k8s-aiTracing a request pathService discovery patternsCross-cluster networking approachesTesting end-to-end connectivityUnderstanding the security architectureAPI key authenticationk8s-ai authenticationMAKDO configurationSession-based securityRBAC and least privilegeInspecting RBAC configurationTesting permission boundariesNetwork segmentation in KinDPort mapping securityTesting network isolationProduction security enhancementsSummary
Chapter 12: Advanced Topics and Future Directions
Superhuman reasoning and strategic foresightWhat is superhuman reasoning?Why is strategic foresight so important?Challenges and limitations of superhuman reasoningImplications for society and ethicsMassive context windows and their implicationsThe evolution of context windowsWhat can you do with million-token context windows?Challenges and trade-offs of large context windowsContext windows and multi-agent systemsThe future – infinite context and continuous learningArchitectures for long-horizon planningThe challenge of planning over timeHierarchical planning and abstractionModel-based planning and world modelsBeyond text – multi-modal models and world simulationTransformers for everything: vision, audio, video, and beyondBiological models: proteins, DNA, and molecular designFrom language models to world models and simulationsMulti-modal multi-agent systemsDesigning for generative user experienceThe robots are comingThe convergence of AI and roboticsEmbodied AI agentsHumanoid robots and general-purpose physical intelligenceMulti-agent and multi-robot coordination in physical spacesChallenges in real-world deploymentThe AI economyCryptocurrencies and AI agents as economic actorsPayment protocols for autonomous agents: AP2 and beyondRobots accelerate the AI self-improvement loopConquering space – the final frontierSummary
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