Amazon Bedrock Agents: Complete Platform Profile

Amazon Bedrock Agents is AWS's fully managed service for building production AI agents that can reason over multi-step tasks, invoke tools, query knowledge bases, and maintain conversation memory — all without managing underlying model infrastructure. For enterprise teams already operating on AWS, it represents a path from prototype to production agent deployment that stays within the IAM, VPC, and CloudWatch governance frameworks they already operate.

Launched in general availability in late 2023 and significantly expanded through 2024 and 2025, Bedrock Agents has become the default choice for AWS-native engineering teams building autonomous systems. This profile covers the platform's architecture, feature set, pricing mechanics, honest limitations, and ideal deployment scenarios.

Browse the full AI agent platform directory to compare Bedrock Agents with other enterprise platforms.

Overview#

Vendor: Amazon Web Services (AWS)
Category: Cloud AI Platform
Founded: 2023 (GA), part of Amazon Bedrock launched 2023
Headquarters: Seattle, Washington
Pricing Model: Pay-as-you-go (input/output tokens + infrastructure components)

Amazon Bedrock Agents is a managed orchestration layer built on top of Amazon Bedrock — AWS's foundation model hosting service. While Bedrock provides access to a catalog of foundation models from Anthropic, Meta, Mistral, Cohere, Amazon, and others, Bedrock Agents adds the agent loop: the reasoning cycle that lets a model plan actions, invoke tools, retrieve knowledge, and iterate toward a goal.

The service abstracts away the operational complexity of running an agent framework in production: no container management, no orchestration logic to maintain, no custom retry handling. The trade-off is a higher-level abstraction that constrains architectural patterns compared to building custom agent systems with frameworks like LangChain or AutoGen.

In the competitive landscape, Bedrock Agents occupies the managed-cloud-native position: competing with Google Vertex AI Agent Builder on the cloud-managed side and with open-source frameworks on the customization side. For AWS-committed enterprises, it is the natural starting point for agent development.

Core Features#

Multi-Model Foundation Model Access#

Bedrock Agents' most significant differentiator from competing managed platforms is model breadth. Agents can be configured with foundation models from Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku), Meta (Llama 3.x series), Mistral AI (Mistral Large, Mixtral), Amazon (Titan, Nova), and Cohere. This means engineering teams can select the model most appropriate for their task profile — cost-performance optimization, context window requirements, domain strength — without changing the orchestration infrastructure.

Model selection is configured at the agent level, and Bedrock's cross-region inference routing automatically handles capacity management and failover between AWS regions, reducing the latency and availability risks associated with single-endpoint model calls.

Action Groups and Tool Use#

Bedrock Agents implements function calling through a construct called Action Groups. Each action group associates a set of callable functions with an OpenAPI schema definition — the agent inspects the schema, decides which function to call based on the task context, constructs the required parameters, and invokes the function via AWS Lambda.

This Lambda-based execution model means action groups can execute any logic that Lambda supports: REST API calls, DynamoDB queries, S3 operations, Step Functions invocations, or calls to on-premises systems via VPN. The boundary between agent reasoning and enterprise system integration is cleanly drawn at the Lambda function boundary, fitting naturally into existing AWS serverless architectures.

For more complex orchestration patterns, Bedrock Agents supports inline code execution (Python in a managed sandbox), enabling agents to perform computation, data transformation, and analysis directly within the agent loop without external Lambda invocations.

Knowledge Bases#

Amazon Bedrock Knowledge Bases provides native retrieval-augmented generation (RAG) integration for Bedrock Agents. Knowledge bases ingest documents from S3, Confluence, SharePoint, Salesforce, and web crawlers, chunk and embed them using configurable embedding models (Amazon Titan Embeddings, Cohere Embed), and store vectors in a managed or self-managed vector store.

Supported vector store backends include Amazon OpenSearch Serverless, Amazon Aurora PostgreSQL with pgvector, Pinecone, Redis Enterprise Cloud, and MongoDB Atlas. This flexibility allows enterprises to use their existing vector database investment rather than adopting a new managed service.

Agents automatically query relevant knowledge bases during task execution, providing grounded responses with citation attribution and configurable relevance thresholds. The knowledge base layer handles the semantic search complexity — enterprises define what knowledge is available; the agent decides when to consult it.

Memory and Session Management#

Bedrock Agents supports two memory mechanisms: session-level memory (conversation history within a single session) and persistent cross-session memory (stored in DynamoDB, surfaced across conversations). Cross-session memory enables agents to remember user preferences, prior decisions, and historical context across separate interactions — a critical capability for enterprise assistants that serve the same users repeatedly.

Memory retention policies, data TTL, and access controls are managed through standard AWS configuration, integrating with existing data governance frameworks rather than introducing new data stores to manage.

Multi-Agent Collaboration#

Bedrock Agents supports a supervisor-subagent orchestration pattern where a supervisor agent decomposes a high-level goal and delegates sub-tasks to specialized subagents. Each subagent operates with its own model, action groups, and knowledge bases — creating a composable, team-of-agents architecture for complex workflows.

This pattern is directly applicable to enterprise scenarios where a single agent cannot reasonably be expert in all required domains (finance analysis, legal review, operational data retrieval) simultaneously.

Human-in-the-Loop Support#

The platform includes a human-in-the-loop return control mechanism: agents can pause execution and return a decision point to the calling application, which can then surface an approval request to a human operator before the agent continues. This is essential for high-stakes actions — financial transactions, system configuration changes, customer communications — where autonomous execution is not acceptable without human review.

Pricing and Plans#

Amazon Bedrock Agents pricing is composed of several metered components:

Foundation Model Tokens:

Charged per 1,000 input and output tokens at the Bedrock model pricing rate
Varies significantly by model: Claude 3 Haiku is substantially cheaper per token than Claude 3 Opus
Cross-region inference incurs no additional routing charge

Knowledge Base Queries:

Charged per knowledge base retrieval operation
Additional charges for vector storage (OpenSearch Serverless OCUs) or self-managed store operational costs

Agent Orchestration:

No separate per-orchestration charge; costs are aggregated through model token consumption

Data Ingestion:

Knowledge base ingestion charged per document processed during sync

For most enterprise workloads, foundation model token costs dominate the total bill. Teams should benchmark their average task token consumption during development to build accurate cost models before production rollout. The ROI measurement guide provides a framework for modeling agent economics against business outcomes.

AWS Committed Spend (Private Pricing) and Reserved Capacity options are available for large-volume enterprise deployments negotiated through AWS account teams.

Strengths#

1. Broadest Foundation Model Selection
No other managed agent platform gives enterprises the choice of Anthropic, Meta, Mistral, Amazon, and Cohere models within a single orchestration service. This avoids vendor lock-in at the model layer and enables cost optimization through model mixing — cheaper models for simple subtasks, higher-capability models for complex reasoning.

2. Native AWS Security Integration
IAM role-based access control, VPC-native deployment, AWS PrivateLink support, CloudTrail audit logging, AWS KMS encryption, and Macie data classification work with Bedrock Agents out of the box. For organizations with mature AWS security postures, governance is solved before the first agent is deployed.

3. Serverless Operational Model
No infrastructure to provision, patch, or scale. Agent execution scales automatically with request volume, and AWS manages model endpoint availability. This reduces the operational burden on platform engineering teams significantly compared to self-managed agent frameworks.

4. Lambda Integration Depth
The action group / Lambda architecture integrates directly with the entire AWS service catalog. Agents can interact with any AWS service that Lambda can reach, without bespoke integration work.

5. Production Maturity
As a GA AWS service with enterprise SLAs, multi-region availability, and AWS Support tier options, Bedrock Agents is suitable for production enterprise deployments that require contractual reliability commitments.

Limitations#

1. AWS-Centric Architecture
Agents are deeply tied to AWS infrastructure (Lambda, S3, OpenSearch, DynamoDB). Organizations with significant workloads on Azure or Google Cloud cannot reuse Bedrock Agents architecture across clouds without rebuilding. Compare the open-source vs commercial agent frameworks if multi-cloud portability is a requirement.

2. Limited Low-Code Access
Unlike Microsoft Copilot Studio, Bedrock Agents requires developer expertise to configure effectively. Business users cannot independently build or maintain agents — this is a developer and ML engineer tool, not a low-code business platform.

3. Constrained Orchestration Patterns
The managed orchestration model, while convenient, restricts how agent reasoning loops can be customized. Teams needing highly bespoke orchestration strategies (custom planning algorithms, specialized memory architectures) may find the abstraction too rigid and prefer frameworks like LangGraph or AutoGen for those workloads.

4. Cold Start Latency on Lambda Actions
Lambda cold starts can introduce latency into action group invocations, particularly for infrequently called functions. Production deployments require attention to Lambda provisioned concurrency and function warm-up strategies to maintain acceptable response times.

Ideal Use Cases#

AWS-Native Enterprise Automation
Engineering teams that want to add autonomous reasoning to existing AWS architectures — pulling data from RDS, writing to DynamoDB, invoking Step Functions, and coordinating across microservices — without standing up a separate agent orchestration layer.

Multi-Model Cost Optimization Workloads
Enterprises managing cost-sensitive AI workloads can configure supervisor agents to route subtasks to the most cost-effective model for each task type, using cheap models for classification and retrieval, and premium models only for final synthesis.

Regulated Data Environments
Healthcare and financial services organizations that require data to remain within specific AWS regions, under specific encryption keys, with IAM-governed access — Bedrock Agents' native AWS security posture satisfies these requirements without additional compliance tooling.

Knowledge Worker Assistants with RAG
Enterprises with large document repositories (legal contracts, technical documentation, policy libraries) can deploy knowledge-base-grounded agents that answer domain-specific questions with source citations, integrated with existing AWS data storage.

Getting Started#

Prerequisites:

AWS account with Bedrock model access enabled (requires requesting access per model family)
IAM roles for agent execution and Lambda invocation
S3 bucket for knowledge base document storage (if RAG is required)

High-Level Approach:

Enable foundation model access for selected models in the AWS Bedrock console
Define agent instructions and select the foundation model
Create action groups with OpenAPI schemas and corresponding Lambda functions
Configure knowledge bases with document sources and embedding model selection
Set up cross-session memory if persistent context is required
Test with the Bedrock console test interface before integrating via the Bedrock Runtime API
Review the enterprise AI agent deployment guide for production readiness checklist

AWS provides a Bedrock Playground for rapid experimentation before committing to agent architecture decisions.

How It Compares#

vs. Google Vertex AI Agent Builder:
Both are managed cloud-native agent platforms. Bedrock Agents has broader foundation model choice; Vertex AI Agent Builder has tighter Gemini integration and Google's search grounding capabilities. AWS-committed enterprises choose Bedrock; GCP-committed enterprises choose Vertex AI. See the Google Vertex AI Agents profile.

vs. LangChain/LangGraph:
LangChain offers dramatically more orchestration flexibility and framework portability but requires managing infrastructure and engineering the agent loop. Bedrock Agents trades customization depth for operational simplicity. See the LangChain vs AutoGen comparison for the self-managed framework landscape.

vs. Microsoft Copilot Studio:
Copilot Studio is the clear choice for Microsoft 365-centric organizations. Bedrock Agents is the choice for AWS-native developer teams. Both are managed platforms, but they serve fundamentally different organizational profiles and skill sets.

Bottom Line#

Amazon Bedrock Agents is the right platform for AWS-committed enterprises that want managed agent infrastructure with genuine foundation model flexibility. Its Lambda-based action architecture, native AWS security integration, and multi-model support make it a production-grade choice for engineering teams building autonomous systems on AWS.

It is not suited for business-user-led agent development, multi-cloud architectures, or teams needing highly customized agent orchestration patterns. The developer expertise requirement is real — this is a platform built by engineers for engineers.

For organizations already running AWS production workloads, Bedrock Agents' operational simplicity and compliance alignment make it the lowest-friction path to enterprise-grade agent deployment. Start with a well-scoped proof-of-concept against a real business use case to validate model costs and action group latency before scaling.

Consider training your agents on proprietary knowledge with the custom training guide to maximize knowledge base effectiveness.