AWS Bedrock Agents Now Support 40+ Tool Integrations and Multi-Agent Workflows

AWS Bedrock Agents Gets a Major Upgrade with Multi-Agent Orchestration

Amazon Web Services has announced a sweeping expansion of its Bedrock Agents platform, introducing native multi-agent orchestration, a marketplace of over 40 pre-built tool integrations, and enterprise-grade governance controls. The updates, announced at AWS AI Summit on March 10, position Bedrock Agents as a comprehensive platform for building, deploying, and managing production AI agent systems at enterprise scale.

Bedrock Agents, originally launched in 2024 as a managed service for building AI agents on AWS, has evolved from a relatively simple retrieval-augmented generation (RAG) tool into a full-featured agent orchestration platform. The March 2026 update represents the largest single feature release in the platform's history and reflects AWS's recognition that AI agents — not standalone model inference — are becoming the primary way enterprises consume generative AI.

Multi-Agent Orchestration

The headline feature is native support for multi-agent workflows, where multiple specialized agents collaborate to handle complex tasks. AWS has implemented three orchestration patterns:

sequenceDiagram
    autonumber
    participant Caller as Caller
    participant Agent as CallSphere Agent
    participant API as CRM API
    participant DB as CRM Database
    participant Webhook as Webhook Listener
    Caller->>Agent: Inbound call begins
    Agent->>Agent: STT plus intent detection
    Agent->>API: Lookup contact by phone
    API->>DB: Read contact record
    DB-->>API: Contact and history
    API-->>Agent: Personalized context
    Agent->>API: Create call activity
    Agent->>API: Update deal stage
    API->>Webhook: Outbound webhook fires
    Webhook-->>Agent: Confirmed
    Agent->>Caller: Spoken confirmation

Supervisor-Worker Pattern

A supervisor agent receives incoming requests, decomposes them into sub-tasks, delegates each sub-task to an appropriate worker agent, monitors execution, and synthesizes results. The supervisor agent maintains a task graph that tracks dependencies, handles failures, and ensures all sub-tasks complete successfully before returning a final result.

This pattern is designed for complex workflows like customer onboarding (where different agents handle identity verification, account creation, regulatory compliance checks, and welcome communications) or procurement processes (where agents manage vendor evaluation, price negotiation, contract review, and purchase order creation).

Sequential Chain Pattern

Agents are arranged in a pipeline where the output of one agent becomes the input of the next. Each agent in the chain can transform, enrich, or act on the data before passing it forward. This pattern is effective for data processing workflows like content moderation (classification agent, policy evaluation agent, action agent) or lead qualification (data enrichment agent, scoring agent, routing agent).

Parallel Fan-Out Pattern

A coordinator agent distributes independent sub-tasks to multiple worker agents that execute concurrently. Results are collected and merged when all workers complete. This pattern is useful for scenarios like competitive analysis (where agents simultaneously research different competitors) or multi-channel notification (where agents handle email, SMS, push, and in-app notifications in parallel).

AWS has implemented these patterns as managed infrastructure, handling the complexities of state management, error recovery, timeout handling, and retry logic that would otherwise require significant custom engineering.

The Tool Marketplace

Perhaps more practically significant than multi-agent orchestration is the new Bedrock Agent Tool Marketplace. AWS has partnered with over 40 technology providers to offer pre-built, managed tool integrations that agents can use out of the box:

Business Applications: Salesforce, HubSpot, ServiceNow, Zendesk, Jira, Confluence, Slack, Microsoft Teams Data and Analytics: Snowflake, Databricks, Tableau, Looker, BigQuery (via cross-cloud connector) Developer Tools: GitHub, GitLab, PagerDuty, Datadog, Splunk Communication: Twilio (voice, SMS, WhatsApp), SendGrid, Mailchimp Commerce: Shopify, Stripe, Square, PayPal Document Processing: Adobe Document Cloud, DocuSign, Notarize

Each tool integration includes:

• Pre-configured authentication (OAuth, API key, or AWS IAM-based)
• Typed action schemas that the agent can discover and use automatically
• Rate limiting and error handling
• Audit logging for all tool invocations
• Configurable permissions that control which actions an agent can perform

"Before the marketplace, connecting an agent to Salesforce required writing custom integration code, managing OAuth tokens, handling rate limits, and dealing with API versioning," explained Swami Sivasubramanian, VP of AI and Data at AWS. "Now it is a single configuration step. Select Salesforce, authorize access, and your agent can immediately read and write Salesforce data."

Enterprise Governance and Compliance

The governance features in this release reflect feedback from AWS's largest enterprise customers, who need fine-grained control over what AI agents can do in production environments:

Agent Policies

A new policy engine allows administrators to define rules that govern agent behavior at the organizational level. Policies can specify:

• Which AWS accounts and regions agents can operate in
• Which foundation models agents can use (preventing unauthorized model access)
• Which tools agents can invoke and with what parameters
• Spending limits (both per-interaction and aggregate)
• Data classification rules (preventing agents from accessing or transmitting data above a specified sensitivity level)

Guardrails Integration

Bedrock Agents now integrates directly with Amazon Bedrock Guardrails, providing content filtering, topic avoidance, and personally identifiable information (PII) detection and redaction across all agent interactions. Guardrails apply to both the agent's inputs and outputs, ensuring that sensitive information is never exposed regardless of the conversation path.

Comprehensive Audit Trail

Every agent interaction generates a detailed audit record that includes the full conversation history, all tool invocations with parameters and results, reasoning traces showing the agent's decision process, cost breakdown by model inference, tool usage, and orchestration overhead, and performance metrics including latency, token usage, and error rates.

Audit records are automatically published to Amazon CloudWatch and can be exported to Amazon S3 for long-term retention, compliance analysis, or integration with security information and event management (SIEM) systems.

Pricing Model

AWS has introduced a simplified pricing model for Bedrock Agents that consolidates the previously separate charges for model inference, knowledge base queries, and action group executions:

• Agent sessions: $0.01 per session initiation
• Model inference: Standard Bedrock pricing based on the selected foundation model
• Tool invocations: $0.002 per tool invocation for marketplace tools, free for custom tools
• Orchestration: $0.005 per orchestration step for multi-agent workflows
• Knowledge base queries: $0.005 per query

AWS estimates that a typical enterprise agent handling customer service inquiries costs between $0.05 and $0.15 per conversation, depending on complexity and the number of tool invocations required.

Customer Adoption

AWS reports that over 15,000 organizations are now using Bedrock Agents in production, up from 3,000 at the time of the last AWS re:Invent conference. Key deployments include:

• A global logistics company using multi-agent workflows to manage shipment tracking, exception handling, and customer communication across 50 countries
• A healthcare payer processing prior authorization requests using agents that coordinate between provider portals, clinical guidelines databases, and claims systems
• A financial services firm using parallel fan-out agents to perform real-time risk assessment across credit, market, and operational risk models simultaneously

"The multi-agent capabilities were the tipping point for us," said the CTO of a major insurance company. "Our claims processing workflow involves seven distinct steps, each requiring different data sources and decision criteria. With multi-agent orchestration, we decompose that into specialized agents that each handle one step, coordinated by a supervisor. It is far more maintainable and reliable than trying to build a single monolithic agent."

Competitive Positioning

The Bedrock Agents expansion intensifies competition with Google Cloud's Vertex AI Agent Builder and Microsoft's Azure AI Agent Service. AWS's primary differentiator is the breadth of its tool marketplace and the depth of integration with the broader AWS ecosystem. Organizations already invested in AWS infrastructure can deploy agents that natively access DynamoDB, SQS, Lambda, Step Functions, and dozens of other AWS services without additional integration work.

Sources

• TechCrunch, "AWS expands Bedrock Agents with multi-agent orchestration and tool marketplace," March 2026
• VentureBeat, "Amazon Bedrock Agents update: 40+ integrations, multi-agent workflows, enterprise governance," March 2026
• The Verge, "AWS is building the infrastructure layer for AI agents," March 2026
• Bloomberg, "Amazon bets big on AI agents with major Bedrock platform expansion," March 2026
• Wired, "AWS wants to be the operating system for enterprise AI agents," March 2026