Building Agentic AI on AWS: From Bedrock Agents to Multi-Agent Orchestration with AgentCore

AWS has become the dominant platform for agentic AI, with Bedrock Agents enabling autonomous planning, tool use, and multi-step execution. The stack spans from single-agent basics to multi-agent orchestration via AgentCore, which provides runtime, memory, identity, and observability. Guardrails and governance tools like IAM and CloudTrail ensure safe production deployment.

2026 is the year AI moved from "answer questions" to "take actions." Agentic AI — systems that autonomously plan, reason, use tools, and execute multi-step tasks — has become the dominant pattern for building intelligent applications on AWS. This post covers the full agentic AI stack on AWS: from single-agent basics to multi-agent orchestration, the infrastructure that runs them, and the guardrails that keep them safe in production. Traditional AI: User asks question → Model generates answer → Done. Agentic AI: User states goal → Agent plans steps → Agent calls tools → Agent evaluates results → Agent iterates → Goal achieved. The difference is autonomy . An agent decides what to do, executes actions, and self-corrects — without human intervention at each step. ┌─────────────────────────────────────────────────────────────┐ │ AGENTIC AI LOOP │ │ │ │ User Goal → Plan → Act → Observe → Reason → Act → Done │ │ ↑ │ │ │ └───────── iterate ──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ APPLICATION LAYER │ │ Amazon Q Business & Developer | Custom agents via Bedrock │ ├─────────────────────────────────────────────────────────────────┤ │ AGENT FRAMEWORKS │ │ Bedrock Agents | Strands Agents SDK | LangGraph on AgentCore │ ├─────────────────────────────────────────────────────────────────┤ │ AGENT INFRASTRUCTURE AgentCore │ │ Runtime | Memory | Identity | Observability | Code Interpreter │ ├─────────────────────────────────────────────────────────────────┤ │ TOOLS & KNOWLEDGE │ │ AgentCore Gateway MCP | Knowledge Bases RAG | Action Groups│ ├─────────────────────────────────────────────────────────────────┤ │ SAFETY & GOVERNANCE │ │ Guardrails | IAM | CloudTrail | Model Evaluation │ ├─────────────────────────────────────────────────────────────────┤ │ FOUNDATION MODELS │ │ Claude | Nova | Llama | Mistral | DeepSeek via Bedrock │ └─────────────────────────────────────────────────────────────────┘ Bedrock Agents is the fully managed way to build AI agents. You define the agent's instructions, connect tools and knowledge, and Bedrock handles the orchestration loop ReAct-style reasoning . | Concept | What It Does | |---|---| Instructions | System prompt that defines agent's role, behavior, and boundaries | Action Groups | Tools the agent can call Lambda functions, APIs, or return-of-control | Knowledge Bases | RAG — grounds agent responses in your data documents, databases | Guardrails | Safety controls content filters, PII masking, denied topics | Memory | Session persistence — agent remembers context across turns | Code Interpreter | Agent can write and execute code to solve problems | Choosing the model: Claude Sonnet or Nova Pro for complex reasoning. Haiku or Nova Micro for simple routing agents. Instruction design: Be specific about the agent's role, what it should NOT do, and how to handle ambiguity. Vague instructions lead to unpredictable behavior. Tool design: Each tool should do ONE thing well. Name them clearly the model uses the name and description to decide when to call them . Include input/output schemas. Example: Defining an action group tool { "actionGroupName": "OrderManagement", "description": "Manages customer orders - lookup, modify, cancel", "apiSchema": { "payload": "openapi-schema.json" }, "actionGroupExecutor": { "lambda": "arn:aws:lambda:us-east-1:123456789:function:order-api" } } For complex problems, a single agent isn't enough. Multi-agent collaboration lets specialized agents work together: ┌──────────────────┐ │ Supervisor Agent │ User ────────→│ Routes tasks │ └────────┬─────────┘ │ ┌──────────────┼──────────────┐ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌───────────┐ ┌──────────────┐ │ Research Agent│ │ Code Agent│ │ Review Agent │ │ RAG + Web │ │ CodeGen │ │ Validation │ └──────────────┘ └───────────┘ └──────────────┘ | Scenario | Single Agent | Multi-Agent | |---|---|---| | FAQ chatbot | ✅ | Overkill | | Code generation only | ✅ | Unnecessary | | Research + summarize + format | ⚠️ Gets messy | ✅ Clean separation | | Customer support billing + tech + shipping | ⚠️ Tool overload | ✅ Specialist routing | | Complex analysis with validation | ⚠️ Context window limits | ✅ Divide and conquer | Rule of thumb: If one agent would need 10 tools or 3 distinct responsibilities, split into multiple agents. AgentCore is the runtime infrastructure for deploying agents at scale. It provides the "boring but critical" capabilities agents need in production: | Component | Purpose | |---|---| Runtime | Serverless execution environment for agents auto-scaling, isolation | Memory | Managed long-term memory across sessions agent remembers past interactions | Identity | Authentication for agent-to-service and agent-to-agent communication | Observability | Traces, metrics, and logs for debugging agent behavior | Code Interpreter | Sandboxed code execution Python/JS for data analysis tasks | Gateway | Converts APIs and Lambda functions into MCP-compatible tools | The Gateway is particularly powerful — it transforms your existing APIs into tools that any agent can discover and use via the Model Context Protocol MCP : This means agents don't need tools baked into their code. They discover capabilities dynamically — add a new API to Gateway, and all connected agents can immediately use it. For teams wanting more control, AWS released Strands Agents SDK — an open-source Python framework for building agents that runs on AgentCore: @tool decorator, auto-generating schemas python from strands import Agent, tool from strands.models.bedrock import BedrockModel @tool def get weather city: str - str: """Get current weather for a city.""" Call weather API return f"Weather in {city}: 22°C, sunny" @tool def create ticket title: str, priority: str - str: """Create a support ticket in the ticketing system.""" Call ticketing API return f"Created ticket: {title} priority: {priority} " agent = Agent model=BedrockModel model id="anthropic.claude-sonnet-4-20250514" , tools= get weather, create ticket , system prompt="You are a helpful assistant that can check weather and create tickets." response = agent "Check the weather in London and create a ticket if it's raining" | Criteria | Bedrock Agents Managed | Strands SDK Code-first | |---|---|---| | Setup complexity | Low console/API | Medium write code | | Customization | Moderate | Full control | | Orchestration logic | AWS-managed ReAct loop | Custom you define the loop | | Multi-agent | Built-in supervisor pattern | Build your own topology | | Deployment | Fully managed | AgentCore Runtime or self-hosted | | Best for | Standard use cases, rapid prototyping | Complex custom logic, advanced patterns | Without knowledge, agents hallucinate. Knowledge Bases provide RAG Retrieval-Augmented Generation : The latest option — fully managed RAG without provisioning anything: Agents that take actions need safety boundaries. Bedrock Guardrails provides: | Policy Type | What It Does | |---|---| Content filters | Block harmful content hate, violence, sexual, misconduct with configurable thresholds | Denied topics | Prevent agent from discussing specific topics competitor info, legal advice, etc. | Word filters | Block specific words or phrases | Sensitive information | Detect and mask PII names, emails, credit cards, SSNs | Grounding check | Detect hallucinations by comparing response against source documents | Contextual grounding | Verify response relevance to the user's query | Guardrails attach to: Key insight: Apply guardrails on BOTH input what users send AND output what agents respond . Users can craft prompts to bypass instructions — guardrails are the defense layer. Best for: Customer support, multi-domain queries. One supervisor routes to specialist workers. Workers don't talk to each other. Best for: Document processing, content creation. Agent A → Agent B → Agent C. Each stage enriches output. Best for: Research, data gathering from multiple sources. Multiple agents work simultaneously, results aggregated. Best for: High-stakes decisions, code review. Generator agent produces output, critic agent evaluates quality, iterate until criteria met. Before deploying agents to production: The agentic AI space on AWS is evolving rapidly: Building agentic AI on AWS in 2026: The shift from "chatbot that answers" to "agent that acts" is the defining pattern of cloud AI in 2026. The infrastructure is ready — the question is what you build on it. Alpesh Kumbhare is an AWS Architect at Atos, specializing in AWS infrastructure automation and cloud AI solutions. Connect on LinkedIn.