Unlock Bedrock InvokeInlineAgent API’s hidden potential with Multi-Agent Orchestrator
Learn how to supercharge AWS Bedrock Agents using Multi-Agent Orchestrator framework and InvokeInlineAgent API. Discover our Bedrock Inline Agent implementation that breaks through knowledge base limitations, enabling dynamic scaling for enterprise AI applications.
Published Nov 28, 2024
You know that feeling when you're trying to stuff too many clothes into a single suitcase? That's exactly how I felt while working with Amazon Bedrock Agents. My clients kept asking me, "Can we implement this with 10 knowledge bases? How about 50?" And there I was, feeling like I was playing enterprise-level Tetris with knowledge bases. The traditional approaches were about as flexible as that overstuffed suitcase - technically possible, but not exactly elegant.
Let me take you behind the scenes of my adventures with Bedrock Agents. Think of them as super-smart digital assistants that actually get things done. Through countless hours in the AWS console, I've watched these agents evolve from simple chatbots into powerful tools that can understand what you're saying and actually do something about it.
What really gets me excited are two killer features:
- Knowledge Bases: I've seen these in action, and they're like giving your agent a library card to every book in your company. I've helped teams go from drowning in customer queries to actually having time for coffee breaks by letting their agents handle the heavy lifting.
- Action Groups: This is where things get really fun. Imagine giving your agent a Swiss Army knife of API calls. I've helped teams set up actions for everything from checking inventory to processing orders. It's like teaching a robot to juggle, but with data!
Then AWS dropped the Inline Agents API. Instead of agents that needed to be configured through the AWS console (you know, the traditional "set it and forget it" approach), we got these nimble, on-the-fly agents that could be tweaked through API calls. Cool, right?
But here's where it got tricky. Like trying to fit an elephant through a keyhole, we kept hitting limits:
- Knowledge bases had caps
- Action groups had restrictions
- And my clients' requirements kept growing
This is where the co-author and I had our "eureka" moment. Working on the Multi-Agent Orchestrator framework, we created something special - our own implementation of Bedrock Inline Agent that completely changes the game.
What we built isn't just another agent - it's like giving your Bedrock agent a PhD in multitasking: instead of being constrained by static configurations, it can dynamically analyze your request and cherry-pick exactly the right combination of action groups and knowledge bases needed for each specific task. Think of it as having a smart assistant that knows all your available tools and knowledge sources, and can instantly assemble the perfect combination for whatever you throw at it.
1. Dynamic Capability Assembly:
- No more pre-configured combinations
- Agents created on-demand like a tech-savvy genie
2. Real Benefits
- Mix capabilities like you're crafting a perfect cocktail
- Perfect for organizations with tool hoarding tendencies
- Use only what you need
In implementing this solution, we've seen organizations transform their knowledge management from a messy junk drawer into a well-organized tool chest. Instead of throwing everything into one massive knowledge base, we help them maintain focused, specific knowledge bases.
Traditional approaches often force you to consolidate all this information into a single knowledge base, which can overwhelm LLMs and lead to less precise responses. When an LLM has to sift through too much irrelevant data, it struggles to identify the most pertinent information for a specific query.
With Bedrock Inline Agent, you can maintain separate, focused knowledge bases and let the agent intelligently select only the ones relevant to each user request. This targeted approach not only improves response accuracy but also makes your system more maintainable and scalable. Your HR team can update their policies without worrying about interfering with technical documentation, and the agent will automatically use the right knowledge base for each query.
Let's see our supercharged agent in action! Here's what happens when we run two different scenarios:
When we ask about
claim-006, watch how our agent springs into action. The Bedrock Inline Agent first selected the combination of Action groups and Knowledge bases that are needed to answer the user’s request:
With trace enabled when calling the invoke inline API, we can see the different Action group and Knowledge bases selected for the agent as part of the trace output:
Response:
Here's the cool part - our agent is smart enough to pick exactly what it needs. For this query, it selected:
- Action Group:
ClaimManagementActionGroup(because we're dealing with claims) - Knowledge Base:
AEXAMPLEID2(our claims documentation)
The agent's thought process was crystal clear: "I need to check a claim status, so I'll grab the claims management tools and relevant documentation."
Now, when we switch gears and ask about the framework itself:
The Bedrock Inline Agent this time has selected a different combination of Action Groups and Knowledge Bases to answer the user’s request:
With trace enabled we can see the different Action group and Knowledge bases selected for the agent as part of the trace output:
Response:
Notice how the agent completely changes its toolkit:
- Action Group:
CodeInterpreterAction(because we might need to demonstrate code) - Knowledge Base:
AEXAMPLEID1(our framework documentation)
This time, the agent thought: "Ah, a question about the framework itself - I'll need technical documentation and potentially the ability to show code examples."
In both cases, our Bedrock Inline Agent dynamically:
- Analyzed the user's question
- Selected the perfect combination of tools and knowledge
- Configured itself on the fly
- Provided relevant, focused answers
This isn't just code execution - it's like having a smart assistant that knows exactly which tools to grab from an infinite toolbox for each specific job. Pretty neat, right?
What we've built with Bedrock's InvokeInlineAgent API isn't just a neat trick - it's a whole new way of thinking about AI agents. Our implementation in the Multi-Agent Orchestrator framework opens up possibilities that were previously locked behind technical limitations.
Whether you're building your first agent or scaling to enterprise-level implementations, we've got your back. Let's build something amazing together!
Ready to supercharge your Bedrock agents? Here's where to get started:
📚 Full Documentation: Check out our comprehensive guide to the Multi-Agent Orchestrator framework
💻 Code Samples:
🚀 Getting Started:
- Follow our QuickStart guide
- Try our ready-to-use example implementations
🤝 Join Our Community:
- Star our GitHub repository
- Share your implementations
- Contribute to the framework
If you find this framework helpful, please consider giving us a star on GitHub. Also we would love to hear your thoughts, so feel free to leave a comment below. And if you have ideas for new features or improvements, don't hesitate to create a feature request on our GitHub repository.
This article was written by Anthony Bernabeu and Corneliu Croitoru, co-authors of Multi-Agent Orchestrator framework.