✨ **多Agent协作自动化企业流程**：企业运营日益复杂，单个AI Agent难以处理跨系统流程。通过多Agent协作，可将专业AI Agent协同工作，实现复杂工作流程的自动化，提高效率和准确性。

🚀 **Salesforce Agentforce与AWS Bedrock Agents的集成优势**：Agentforce提供基于企业数据和上下文的AI Agent能力，而AWS Bedrock Agents则能访问和操作跨多个系统的信息。两者结合，能够构建跨越Salesforce和AWS环境的智能Agent，充分发挥双方优势，实现更智能、更全面的AI工作流。

🔗 **灵活的集成模式实现跨系统交互**：Agentforce与Amazon Bedrock Agents可以灵活协作，例如Agentforce作为主协调者，管理端到端客户流程，并通过自定义操作将专门任务委托给Amazon Bedrock Agents。这种模式能够协调跨系统的数据和服务的访问，从而最大化Salesforce和AWS的效益。

💡 **通过知识库和Lambda实现具体场景自动化**：文章以IoT传感器数据处理为例，展示了如何利用Amazon Bedrock Knowledge Bases查询Amazon Redshift数据库获取传感器数据上下文，并由Bedrock Agent根据数据异常触发Agentforce创建服务案例。Lambda函数作为Agentforce的入口，实现了Bedrock Agent与Agentforce之间的安全通信和数据转换。

🔧 **构建此解决方案的关键技术与配置**：成功构建此集成需要AWS账户、Amazon Bedrock访问权限、Redshift集群、Agentforce系统以及Lambda和IAM的知识。详细的配置步骤包括准备Redshift数据、创建特定的IAM角色、设置Amazon Bedrock知识库和Agent，以及部署Lambda函数，确保数据安全和流程顺畅。

AI agents are rapidly transforming enterprise operations. Although a single agent can perform specific tasks effectively, complex business processes often span multiple systems, requiring data retrieval, analysis, decision-making, and action execution across different systems. With multi-agent collaboration, specialized AI agents can work together to automate intricate workflows.

This post explores a practical collaboration, integrating Salesforce Agentforce with Amazon Bedrock Agents and Amazon Redshift, to automate enterprise workflows.

Multi-agent collaboration in Enterprise AI

Enterprise environments today are complex, featuring diverse technologies across multiple systems. Salesforce and AWS provide distinct advantages to customers. Many organizations already maintain significant infrastructure on AWS, including data, AI, and various business applications such as ERP, finance, supply chain, HRMS, and workforce management systems. Agentforce delivers powerful AI-driven agent capabilities that are grounded in enterprise context and data. While Salesforce provides a rich source of trusted business data, customers increasingly need agents that can access and act on information across multiple systems. By integrating AWS-powered AI services into Agentforce, organizations can orchestrate intelligent agents that operate across Salesforce and AWS, unlocking the strengths of both.

Agentforce and Amazon Bedrock Agents can work together in flexible ways, leveraging the unique strengths of both platforms to deliver smarter, more comprehensive AI workflows. Example collaboration models include:

Agentforce as the primary orchestrator:
Manages end to end customer-oriented workflows Delegates specialized tasks to Amazon Bedrock Agents as needed through custom actions Coordinates access to external data and services across systems

This integration creates a more powerful solution that maximizes the benefits of both Salesforce and AWS, so you can achieve better business outcomes through enhanced AI capabilities and cross-system functionality.

Agentforce overview

Agentforce brings digital labor to every employee, department, and business process, augmenting teams and elevating customer experiences.It works seamlessly with your existing applications, data, and business logic to take meaningful action across the enterprise. And because it’s built on the trusted Salesforce platform, your data stays secure, governed, and in your control. With Agentforce, you can:

Deploy prebuilt agents designed for specific roles, industries, or use cases Enable agents to take action with existing workflows, code, and APIs Connect your agents to enterprise data securely Deliver accurate and grounded outcomes through the Atlas Reasoning Engine

Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases overview

Amazon Bedrock is a fully managed AWS service offering access to high-performing foundation models (FMs) from various AI companies through a single API. In this post, we discuss the following features:

Amazon Bedrock Agents – Managed AI agents use FMs to understand user requests, break down complex tasks into steps, maintain conversation context, and orchestrate actions. They can interact with company systems and data sources through APIs (configured through action groups) and access information through knowledge bases. You provide instructions in natural language, select an FM, and configure data sources and tools (APIs), and Amazon Bedrock handles the orchestration. Amazon Bedrock Knowledge Bases – This capability enables agents to perform Retrieval Augmented Generation (RAG) using your company’s private data sources. You connect the knowledge base to your data hosted in AWS, such as in Amazon Simple Storage Service (Amazon S3) or Amazon Redshift, and it automatically handles the vectorization and retrieval process. When asked a question or given a task, the agent can query the knowledge base to find relevant information, providing more accurate, context-aware responses and decisions without needing to retrain the underlying FM.

Agentforce and Amazon Bedrock Agent integration patterns

Agentforce can call Amazon Bedrock agents in different ways, allowing flexibility to build different architectures. The following diagram illustrates synchronous and asynchronous patterns.

For a synchronous or request-reply interaction, Agentforce uses custom agent actions facilitated by External Services, Apex Invocable Methods, or Flow to call an Amazon Bedrock agent. The authentication to AWS is facilitated using named credentials. Named credentials are designed to securely manage authentication details for external services integrated with Salesforce. They alleviate the need to hardcode sensitive information like user names and passwords, minimizing the risk of exposure and potential data breaches. This separation of credentials from the application code can significantly enhance security posture. Named credentials streamline integration by providing a centralized and consistent method for handling authentication, reducing complexity and potential errors. You can use Salesforce Private Connect to provide a secure private connection with AWS using AWS PrivateLink. Refer to Private Integration Between Salesforce and Amazon API Gateway for additional details.

For asynchronous calls, Agentforce uses Salesforce Event Relay and Flow with Amazon EventBridge to call an Amazon Bedrock agent.

In this post, we discuss the synchronous call pattern. We encourage you to explore Salesforce Event Relay with EventBridge to build event-driven agentic AI workflows. Agentforce also offers the Agent API, which makes it straightforward to call an Agentforce agent from an Amazon Bedrock agent, using EventBridge API destinations, for bi-directional agentic AI workflows.

Solution overview

To illustrate the multi-agent collaboration between Agentforce and AWS, we use the following architecture, which provides access to Internet of Things (IoT) sensor data to the Agentforce agent and handles potentially erroneous sensor readings using a multi-agent approach.

The example workflow consists of the following steps:

Coral Cloud has equipped their rooms with smart air conditioners and temperature sensors. These IoT devices capture critical information such as room temperature and error code and store it in Coral Cloud’s AWS database in Amazon Redshift. Agentforce agent calls an Amazon Bedrock agent through the Agent Wrapper API with questions such as “What is the temperature in room 123” to answer customer questions related to the comfort of the room. This API is implemented as an AWS Lambda function, acting as the entry point in the AWS Cloud. The Amazon Bedrock agent, upon receiving the request, needs context. It queries its configured knowledge base by generating the necessary SQL query. The knowledge base is connected to a Redshift database containing historical sensor data or contextual information (like the sensor’s thresholds and maintenance history). It retrieves relevant information based on the agent’s query and responds back with an answer. With the initial data and the context from the knowledge base, the Amazon Bedrock agent uses its underlying FM and natural language instructions to decide the appropriate action. In this scenario, detecting an error prompts it to create a case when it receives erroneous readings from a sensor. The action group contains the Agentforce Agent Wrapper Lambda function. The Amazon Bedrock agent securely passes the necessary details (like which sensor or room needs a case) to this function. The Agentforce Agent Wrapper Lambda function acts as an adapter. It translates the request from the Amazon Bedrock agent into the specific format required by the Agentforce service‘s API or interface. The Lambda function calls Agentforce, instructing it to create a case associated with the contact or account linked to the sensor that sent the erroneous reading. Agentforce uses its internal logic (agent, topics, and actions) to create or escalate the case within Salesforce.

This workflow demonstrates how Amazon Bedrock Agents orchestrates tasks, using Amazon Bedrock Knowledge Bases for context and action groups (through Lambda) to interact with Agentforce to complete the end-to-end process.

Prerequisites

Before building this architecture, make sure you have the following:

AWS account – An active AWS account with permissions to use Amazon Bedrock, Lambda, Amazon Redshift, AWS Identity and Access Management (IAM), and API Gateway. Amazon Bedrock access – Access to Amazon Bedrock Agents and to Anthropic’s Claude 3.5 Haiku v1 enabled in your chosen AWS Region. Redshift resources – An operational Redshift cluster or Amazon Redshift Serverless endpoint. The relevant tables containing sensor data (historical readings, sensor thresholds, and maintenance history) must be created and populated. Agentforce system – Access to and understanding of the Agentforce system, including how to configure it. You can sign up for a developer edition with Agentforce and Data Cloud. Lambda knowledge – Familiarity with creating, deploying, and managing Lambda functions (using Python). IAM roles and policies – Understanding of how to create IAM roles with the necessary permissions for Amazon Bedrock Agents, Lambda functions (to call Amazon Bedrock, Amazon Redshift, and the Agentforce API), and Amazon Bedrock Knowledge Bases.

Prepare Amazon Redshift data

Make sure your data is structured and available in your Redshift instance. Note the database name, credentials, and table and column names.

Create IAM roles

For this post, we create two IAM roles:

custom_AmazonBedrockExecutionRoleForAgents:
Attach the following AWS managed policies to the role:
AmazonBedrockFullAccess AmazonRedshiftDataFullAccess
In the trust relationship, provide the following trust policy (provide your AWS account ID):

{    "Version": "2012-10-17",    "Statement": [        {            "Sid": "AmazonBedrockAgentBedrockFoundationModelPolicyProd",            "Effect": "Allow",            "Principal": {                "Service": "bedrock.amazonaws.com"            },            "Action": "sts:AssumeRole",            "Condition": {                "StringEquals": {                    "aws:SourceAccount": "YOUR_ACCOUNT_ID"                }            }        }    ]}

custom_AWSLambdaExecutionRole:
Attach the following AWS managed policies to the role:
AmazonBedrockFullAccess AmazonLambdaBasicExecutionRole
In the trust relationship, provide the following trust policy (provide your AWS account ID):

{    "Version": "2012-10-17",   "Statement": [       {           "Effect": "Allow",           "Principal": {               "Service": "lambda.amazonaws.com"           },           "Action": "sts:AssumeRole",           "Condition": {               "StringEquals": {                   "aws:SourceAccount": "YOUR_ACCOUNT_ID"               }           }       }   ]}

Create an Amazon Bedrock knowledge base

Complete the following steps to create an Amazon Bedrock knowledge base:

On the Amazon Bedrock console, choose Knowledge Bases in the navigation pane. Choose Create and Knowledge Base with structured data store.

On the Provide Knowledge Base details page, provide the following information:
Enter a name and optional description. For Query engine, select Amazon Redshift. For IAM permissions, select Use an existing service role and choose custom_AmazonBedrockExecutionRoleForAgents. Choose Next.

For Query engine connection details, select Redshift provisioned and choose your cluster. For Authentication, select IAM Role. For Storage configuration, select Amazon Redshift database and Redshift database list. On the Configure query engine page, provide the following information:
Provide table and column descriptions. The following is an example.
Choose Create Knowledge Base.
After you create the knowledge base, open the Redshift query editor and grant permissions for the role to access Redshift tables by running the following queries:

CREATE USER "IAMR:custom_AmazonBedrockExecutionRoleForAgents" WITH PASSWORD DISABLE; GRANT SELECT ON ALL TABLES IN SCHEMA dev.knowledgebase TO "IAMR:custom_AmazonBedrockExecutionRoleForAgents"; GRANT USAGE ON SCHEMA dev.knowledgebase TO "IAMR:custom_AmazonBedrockExecutionRoleForAgents";

For more information, refer to set up your query engine and permissions for creating a knowledge base with structured data store.

5. Choose Sync to sync the query engine.

Make sure the status shows as Complete before moving to the next steps.

When the sync is complete, choose Test Knowledge Base. Select Retrieval and response generation: data sources and model and choose Claude 3.5 Haiku for the model. Enter a question about your data and make sure you get a valid answer.

Create an Amazon Bedrock agent

Complete the following steps to create an Amazon Bedrock agent:

On the Amazon Bedrock console, choose Agents in the navigation pane. Choose Create agent. On the Agent details page, provide the following information:
Enter a name and optional description. For Agent resource role, select Use an existing service role and choose custom_AmazonBedrockExecutionRoleForAgents.

Provide detailed instructions for your agent. The following is an example:

You are an IoT device monitoring and alerting agent. You have access to the structured data containing reading, maintenance, threshold data for IoT devices. You answer questions about device reading, maintenance schedule and thresholds. You can also create case via Agentforce. When you receive comma separated values parse them as device_id, temperature, voltage, connectivity and error_code. First check if the temperature is less than min temperature, more than max temperature and connectivity is more than the connectivity threshold for the product associated with the device id. If there is an error code, send information to agentforce to create case. The information sent to agentforce should include device readings such as device id, error code. It should also include the threshold values related to the product associated with the device such as min temperature, max temperature and connectivity, In response to your call to agentforce just return the summary of the information provided with all the attributes provided. Do not omit any information in the response. Do not include the word escalated in agent.

Choose Save to save the agent. Add the knowledge base you created in previous step to this agent.

Provide detailed instructions about the knowledge base for the agent.

Choose Save and then choose Prepare the agent. Test the agent by asking a question (in the following example, we ask about sensor readings).

Choose Create alias. On the Create alias page, provide the following information:
Enter an alias name and optional description. For Associate version, select Create a new version and associate it to this alias. For Select throughput, select On-demand. Choose Create alias.

Note down the agent ID, which you will use in subsequent steps. Note down the alias ID and agent ID, which you will use in subsequent steps.

Create a Lambda function

Complete the following steps to create a Lambda function to receive requests from Agentforce:

On the Lambda console, choose Functions in the navigation pane. Choose Create function. Configure the function with the following logic to receive requests through API Gateway and call Amazon Bedrock agents:

import boto3import uuidimport jsonimport pprintimport tracebackimport timeimport loggingfrom agent_utils import invoke_agent_generate_responselogger = logging.getLogger()logger.setLevel(logging.INFO)bedrock_agent_runtime_client = boto3.client(service_name="bedrock-agent-runtime",region_name="REGION_NAME", # replace with the region name from your account)def lambda_handler(event, context):    logger.info(event)    body = event['body']    input_text = json.loads(body)['inputText']    agent_id = 'XXXXXXXX' # replace with the agent id from your account    agent_alias_id = 'XXXXXXX' # replace with the alias id from your account    session_id:str = str(uuid.uuid1()) # random identifier    enable_trace:bool = False    end_session:bool = False    final_answer = None    response = call_agent(input_text, agent_id, agent_alias_id)    print("response : ")    print(response)     return {        'headers': {            'Content-Type' : 'application/json',            'Access-Control-Allow-Headers': '*',            'Access-Control-Allow-Origin': '*',            'Access-Control-Allow-Methods': '*'        },                'statusCode': 200,        'body': json.dumps({"outputText" :  response  })    }def call_agent(inputText, agentId, agentAliasId):     session_id = str(uuid.uuid1())    enable_trace = False    end_session = False    while True:        try:            agent_response = bedrock_agent_runtime_client.invoke_agent(                inputText=inputText,                agentId=agentId,                agentAliasId=agentAliasId,                                sessionId=session_id,                enableTrace=enable_trace,                endSession=end_session            )            logger.info("Agent raw response:")            pprint.pprint(agent_response)            if 'completion' not in agent_response:                raise ValueError("Missing 'completion' in agent response")            for event in agent_response['completion']:                chunk = event.get('chunk')                # print('chunk: ', chunk)                if chunk:                    decoded_bytes = chunk.get("bytes").decode()                    # print('bytes: ', decoded_bytes)                    return decoded_bytes        except Exception as e:            print(traceback.format_exc())            return f"Error: {str(e)}"

Define the necessary IAM permissions by assigning custom_AWSLambdaExecutionRole.

Create a REST API

Complete the following steps to create a REST API in API Gateway:

On the API Gateway console, create a REST API with proxy integration.

Enable API key required to protect the API from unauthenticated access.

Configure the usage plan and API key. For more details, see Set up API keys for REST APIs in API Gateway. Deploy the API. Note down the Invoke URL to use in subsequent steps.

Create named credentials in Salesforce

Now that you have created an Amazon Bedrock agent with an API Gateway endpoint and Lambda wrapper, let’s complete the configuration on the Salesforce side. Complete the following steps:

Log in to Salesforce. Navigate to Setup, Security, Named Credentials. On the External Credentials tab, choose New.

Provide the following information:
Enter a label and name. For Authentication Protocol, choose Custom. Choose Save.

Open the External Credentials entry to provide additional details:
Under Principals, create a new principal and provide the parameter name and value.

Under Custom Headers, create a new entry and provide a name and value. Choose Save.

Now you can grant access to the agent user to access these credentials.

Navigate to Setup, Users, User Profile, Enabled External Credential Principal Access and add the external credential principal you created to the allow list.

Choose New to create a named credentials entry. Provide details such as label, name, the URL of the API Gateway endpoint, and authentication protocol, then choose Save.

You can optionally use Salesforce Private Connect with PrivateLink to provide a secure private connection with. This allows critical data to flow from the Salesforce environment to AWS without using the public internet.

Add an external service in Salesforce

Complete the following steps to add an external service in Salesforce:

In Salesforce, navigate to Setup, Integrations, External Services and choose Add an External Service. For Select an API source, choose From API Specification.

On the Edit an External Service page, provide the following information:
Enter a name and optional description. For Service Schema, choose Upload from local. For Select a Named Credential, choose the named credential you created.

Upload an Open API specification for the API Gateway endpoint. See the following example:

openapi: 3.0.0info:  title: Bedrock Agent Wrapper API  version: 1.0.0  description: Bedrock Agent Wrapper APIpaths:  /proxy:    post:      operationId: call-bedrock-agent      summary: Call Bedrock Agent      description: Call Bedrock Agent      requestBody:        description: input        required: true        content:          application/json:            schema:              $ref: '#/components/schemas/input'      responses:        '200':          description: Successful response          content:            application/json:              schema:                $ref: '#/components/schemas/output'        '500':          description: Server errorcomponents:  schemas:    input:      type: object      properties:        inputText:          type: string        agentId:          type: string        agentAlias:          type: string    output:      type: object      properties:        outputText:          type: string

Choose Save and Next. Enable the operation to make it available for Agentforce to invoke. Choose Finish.

Create an Agentforce agent action to use the external service

Complete the following steps to create an Agentforce agent action:

In Salesforce, navigate to Setup, Agentforce, Einstein Generative AI, Agentforce Studio, Agentforce Assets. On the Actions tab, choose New Agent Action. Under Connect to an existing action, provide the following information:
For Reference Action Type, choose API. For Reference Action Category, choose External Services. For Reference Action, choose the Call Bedrock Agent action that you configured. Enter an agent action label and API name. Choose Next.

Provide the following information to complete the agent action configuration:
For Agent Action Instructions, enter Call Bedrock Agent to get the information about device readings, sensor readings, maintenance or threshold information. For Loading Text, enter Calling Bedrock Agent. Under Input, for Body, enter Provide the input in the input Text field. Under Outputs, for 200, enter Successful response.

Save the agent action.

Configure the Agentforce agent to use the agent action

Complete the following steps to configure the Agentforce agent to use the agent action:

In Salesforce, navigate to Setup, Agentforce, Einstein Generative AI, Agentforce Studio, Agentforce Agents and open the agent in Agent Builder. Create a new topic. On the Topic Configuration tab, provide the following information:
For Name, enter Device Information. For Classification Description, enter This topic handles inquiries related to device and sensor information, including reading, maintenance, and threshold. For Scope, enter Your job is only to provide information about device readings, sensor readings, device maintenance, sensor maintenance, and threshold. Do not attempt to address issues outside of providing device information. For Instructions, enter the following:

If a user asks for device readings or sensor readings, provide the information.If a user asks for device maintenance or sensor maintenance, provide the information.When searching for device information, include the device or sensor id and any relevant keywords in your search query.

On the This Topic’s Actions tab, choose New and Add from Asset Library.

Choose the Call Bedrock Agent action.

Activate the agent and enter a question, such as “What is the latest reading for sensor with device id CITDEV003.”

The agent will indicate that it is calling the Amazon Bedrock agent, as shown in the following screenshot.

The agent will fetch the information using the Amazon Bedrock agent from the associated knowledge base.

Clean up

To avoid additional costs, delete the resources that you created when you no longer need them:

Delete the Amazon Bedrock knowledge base:
On the Amazon Bedrock console, choose Knowledge Bases in the navigation pane. Select the knowledge base you created and choose Delete.
Delete the Amazon Bedrock agent:
On the Amazon Bedrock console, choose Agents in the navigation pane. Select the agent you created and choose Delete.
Delete the Lambda function:
On the Lambda console, choose Functions in the navigation pane. Select the function you created and choose Delete.
Delete the REST API:
On the API Gateway console, choose APIs in the navigation pane. Select the REST API you created and choose Delete.

Conclusion

In this post, we described an architecture that demonstrates the power of combining AI services on AWS with Agentforce. By using Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases for contextual understanding through RAG, and Lambda functions and API Gateway to bridge interactions with Agentforce, businesses can build sophisticated, automated workflows. As AI capabilities continue to grow, such collaborative multi-agent systems will become increasingly central to enterprise automation strategies. In an upcoming post, we will show you how to build the asynchronous integration pattern from Agentforce to Amazon Bedrock using Salesforce Event Relay.

To get started, see Become an Agentblazer Innovator and refer to How Amazon Bedrock Agents works.

About the authors

Yogesh Dhimate is a Sr. Partner Solutions Architect at AWS, leading technology partnership with Salesforce. Prior to joining AWS, Yogesh worked with leading companies including Salesforce driving their industry solution initiatives. With over 20 years of experience in product management and solutions architecture Yogesh brings unique perspective in cloud computing and artificial intelligence.

Kranthi Pullagurla has over 20+ years’ experience across Application Integration and Cloud Migrations across Multiple Cloud providers. He works with AWS Partners to build solutions on AWS that our joint customers can use. Prior to joining AWS, Kranthi was a strategic advisor at MuleSoft (now Salesforce). Kranthi has experience advising C-level customer executives on their digital transformation journey in the cloud.

Shitij Agarwal is a Partner Solutions Architect at AWS. He creates joint solutions with strategic ISV partners to deliver value to customers. When not at work, he is busy exploring New York city and the hiking trails that surround it, and going on bike rides.

Ross Belmont is a Senior Director of Product Management at Salesforce covering Platform Data Services. He has more than 15 years of experience with the Salesforce ecosystem.

Sharda Rao is a Senior Director of Product Management at Salesforce covering Agentforce Go To Market strategy

Hunter Reh is an AI Architect at Salesforce and a passionate builder who has developed over 100 agents since the launch of Agentforce. Outside of work, he enjoys exploring new trails on his bike or getting lost in a great book.