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12 Must-Know Agentic AI Terms in AWS Context

A practical AWS-focused breakdown of key Agentic AI concepts from MCP to multi-agent systems mapped to real AWS services like Bedrock, Lambda, Step Functions, and SageMaker.

Santhosh K Ramurthy

System Integrator
  1. MCP (Model Context Protocol)
    In AWS terms, MCP acts like a standard integration layer between agents and services.
    Think:
    Amazon API Gateway
    AWS Lambda functions
    Amazon S3, DynamoDB, and external SaaS APIs
    It standardizes how agents “plug into” AWS services and enterprise tools.
  2. Agent Loop
    On AWS, the agent loop runs continuously using services like:
    Amazon Bedrock (reasoning layer)
    AWS Lambda (execution layer)
    Amazon EventBridge (event triggers)
    Flow:
    Event → Reason (Bedrock) → Action (Lambda) → Observation (CloudWatch/S3 logs)
  3. Tool Use
    Tool use is where agents call AWS services directly:
    Lambda functions for computation
    DynamoDB for state
    S3 for storage
    Athena for querying data
    This is what turns an LLM into an operational system.
  4. Orchestrator
    In AWS, orchestration is commonly handled by:
    AWS Step Functions
    Amazon Bedrock Agents (agent orchestration layer)
    It breaks a goal into workflows and routes tasks to sub-components.
  5. Subagent
    A subagent is often implemented as:
    A dedicated Bedrock agent
    A Lambda-powered micro-agent
    A containerized service on ECS or EKS
    Each subagent handles a single responsibility.
  6. Memory
    AWS-based memory systems typically include:
    Amazon DynamoDB (structured memory)
    Amazon S3 (long-term logs/documents)
    Amazon OpenSearch / vector databases (semantic memory)
    This enables both short-term and long-term reasoning.
  7. Grounding
    Grounding on AWS ensures responses are based on trusted data sources like:
    Amazon S3 data lakes
    AWS Glue catalogs
    Enterprise databases via RDS/Aurora
    Often combined with RAG pipelines using Bedrock.
  8. Guardrails
    AWS provides native safety controls via:
    Amazon Bedrock Guardrails
    IAM policies
    Lambda execution permissions
    They define what the agent is allowed to access or execute.
  9. Sandboxing
    Sandboxing in AWS is achieved using:
    AWS Lambda isolated runtime
    ECS Fargate containers
    Secure VPC environments
    It ensures safe execution before production impact.
  10. Human-in-the-Loop
    Implemented using:
    Amazon SNS (approvals/notifications)
    AWS Step Functions manual approval steps
    Amazon A2I (Augmented AI workflows)
    Used for high-risk decisions like financial actions or deployments.
  11. Context Window
    While the model defines the context window, AWS extends it using:
    External memory (S3, DynamoDB)
    RAG pipelines via Amazon Bedrock Knowledge Bases
    So agents are not limited by token size alone.
  12. Multi-Agent Systems
    On AWS, multi-agent systems are built using:
    Amazon Bedrock multi-agent orchestration
    Step Functions distributed workflows
    EventBridge event-driven coordination
    Each agent is a service working like microservices for intelligence.
# agentic-ai# agents# agent-toolkit# agent-to-agent
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