I had the chance to attend AWS Summit Bangalore 2025, and one presentation in particular caught my attention: a thorough examination of multi-agent collaboration utilizing Amazon Bedrock Agents. The discussion focused on how to create intelligent, self-governing AI systems that can plan, reason, and act in addition to simply answering questions. Let me tell you what I discovered.
Key Highlights
Multi-agent collaboration permits specialized agents to collaborate instead of creating a single, large, complex agent.
Two orchestration strategies: Supervisor Agent for step-by-step task execution with routing and ReAct (Reasoning and Acting)
Start small principle: Start with single-purpose, targeted agents and progressively increase their capabilities.
Real-world applications: From marketing campaigns involving numerous specialized agents to HR automation
Built-in security and scalability: Included were enterprise-grade observability, guardrails, and memory retention.
Understanding Gen AI Agents
What is a Gen AI Agent, then? Imagine it as a self-governing, intelligent system that can do more than just reply to chat messages. These agents are able to plan complicated activities, use tools, access enterprise data, and carry them out step-by-step. Their practical design allows them to handle real-world company issues, such as document processing, customer service workflows, and HR inquiries.
Capability is the primary distinction between an agent and a standard AI chatbot. "What's my vacation balance?" may be answered by a chatbot, but in a single discussion, an agent can check several systems, determine how many days you have left, confirm business policies, and even assist you in scheduling time off.
The Building Blocks of Amazon Bedrock Agents
A number of fundamental elements offered by Amazon Bedrock make it simple to construct these agents. First, you are not restricted to a single AI model; you can choose from a variety of basis models. Depending on your particular requirements, such as price, speed, or capability, you can choose from top models.
Knowledge bases and memory come next. For agents to function well, they require context. With Retrieval Augmented Generation (RAG), knowledge bases give agents access to company documents, policies, and data. This means that instead of depending only on its training data, your agent may retrieve current, correct information from your company's knowledge repositories.
Action groups and resources are also available on the platform. These are your agent's "hands", Lambda functions, API calls, or code interpreters that let the agent to accomplish tasks rather than merely discuss them. Lastly, there is built-in observability, debugging, and tracing, all of which are essential when overseeing several agents cooperating.
Starting Small and Focused
The significance of beginning small was one of the session's main lessons. It is advised to develop targeted agents that excel at particular activities rather than creating a single, all-knowing agent.
Take an HR case, for instance. A Time Off Agent would handle vacation requests, a LOA Expert would handle leave of absence procedures, and a Promotion Agent would handle conversations about career progression. This would eliminate the need for a single mega-agent to handle everything. Each agent has a distinct set of activities that are specific to its area and clear, unambiguous instructions.
What the lecturer referred to as the "Crawl-Walk-Run Methodology" is what this strategy uses. You begin with a basic use case, make sure it functions, and then progressively increase the capabilities. Just four tasks could be performed by the HR Time Off Agent at first: calculating vacation balance, verifying sick leave, scheduling sick time, and scheduling vacation time. Maintainable, testable, and easy to use.
The Challenge of Complexity
Naturally, creating AI agents is not without its difficulties. Three key problems arise as agents get more complex. First, the coding becomes brittle and difficult to maintain, and you need complex prompts to prevent hallucinations. Second, agents may become perplexed and respond inconsistently, call the wrong tools, or pass the wrong arguments. Third, the necessity for frontier models and the growth of prompt sizes due to repeated actions make agents slower and more costly as you add more capabilities.
This is where multi-agent collaboration becomes essential.
Multi-Agent Collaboration: The Solution
Amazon Bedrock allows for multi-agent systems, in which specialized agents collaborate rather than relying on a single agent to handle all tasks. This is made scalable by the platform, which makes it simple to put together agents and knowledge bases, plan and carry out complex tasks involving numerous agents, unify conversations with integrated intent classification, offer observability throughout all agent interactions, and protect privacy and security with guardrails.
There are two main orchestration strategies for coordinating multiple agents.
Supervisor Agent with Routing
In the first approach, a supervisor agent serves as a router. An HR Agent receives a user's question, such as "How much vacation time do I have left?" and forwards it to an intent-classified, optimized router. Whichever specialized agent Time Off Specialist, LOA Expert, or Promotion Expert should handle the request is decided by the router, which then routes appropriately.
To build this supervisor, select a fast model, such as small-fast-model-1.0, and set the agent type to SUPERVISOR_ROUTER. Next, you outline each sub-agent's responsibilities in their collaboration descriptions. These descriptions are used by the router to make wise routing choices that guarantee the appropriate expert receives inquiries.
ReAct: Step-by-Step Reasoning
The default orchestration approach, known as ReAct (Reasoning and Acting), is the second option. This method approaches difficult tasks methodically. The agent goes through a thought process for each task, decides what needs to be done, carries out those actions to achieve results, and then modifies its plan in light of those outcomes. This loop keeps going till the work is completed.
This approach is particularly powerful for complex, multi-step workflows where the next action depends on previous results.
Real-World Use Case: Complex Marketing Automation
Automating extensive marketing efforts was a compelling use case for supervisor agents that was demonstrated during the event. For example, "Give me a complete marketing strategy for my new product XYZ" is what you may ask an agent.
The supervisor agent would dynamically generate a plan involving multiple specialized sub-agents:
Marketing Strategist conducts market research including competitors
Content Creator develops detailed project summaries and target personas
Style Guide ensures brand consistency across materials
Campaign Feedback provides analytical insights
Market Analyst crunches the numbers
Copy Editor polishes all written content
Project Storage Agent manages documents
Video Generation Expert creates video content
Every sub-agent has unique skills and knowledge bases. This entire workflow is coordinated by the supervisor, who ensures that every specialized agent contributes their expertise to deliver a comprehensive marketing strategy.
Unifying Customer Experience
Creating consistent client experiences across many services is another effective use. A single conversational interface connects to specialist agents for time off, leave of absence, and promotions, eliminating the need for users to navigate distinct programs for each purpose. From the customer's point of view, it's a single, smooth interaction, even though each agent keeps up their unique skill and knowledge base.
This design keeps the backend manageable while significantly enhancing the user experience. Without affecting the system as a whole, each agent can be separately updated, tested, and deployed.
Best Practices for Building Agents
The session concluded with practical best practices for anyone building with Amazon Bedrock Agents :
Laying the groundwork: Know your users before writing code. Prioritize creating the user experience by considering the outcomes consumers anticipate and the appropriate flow of interactions.
Defining scope and sample interactions: Give precise directions and definitions. The nemesis of trustworthy AI agents is ambiguity. Give examples of encounters that demonstrate just how the agent has to act in various situations.
Building small and focused agents: Avoid the urge to construct everything at once. Make agents that are narrowly focused and highly skilled at particular activities. This enables testing, debugging, and improvement.
Following Crawl-Walk-Run Methodology: Begin with the most basic version. Verify that it functions. Add features gradually after that. This iterative process speeds up learning and lowers risk.
Conclusion
Advanced multi-agent systems are now accessible without the need for in-depth AI knowledge thanks to Amazon Bedrock Agents. The platform offers crucial building elements, manages complex orchestration, and incorporates observability tools for production systems. The creation of specialized agents that work well together and each contribute domain knowledge to address challenging issues is the key to the future of enterprise AI. The system is prepared for careful deployment; start small, concentrate on certain use cases, and utilize multi-agent collaboration as requirements increase.
At the AWS Summit Bangalore 2025, this was just one of many exciting sessions. Stay tuned for more insights from other sessions I attended!
References
Event: AWS Summit Bangalore 2025
Topic: Amazon Bedrock
Date: May 08, 2025
Location: KTPO Exhibition Center in Whitefield, Bangalore
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