Amazon Bedrock AgentCore Fundamentals | Deploy AI Agents at Scale

本站添加: 2026-05-20

[00:00:02]Hey everyone, welcome to Amazon Bedrock Agent Core Workshop series.

[00:00:06]Before we dive in, let me quickly introduce myself. I'm Vijender Singh, a cloud and AI specialist with over 8 years of experience in designing and implementing NLP, LLM, and machine learning solutions.

[00:00:20]So, let's jump into it. What is Amazon Bedrock Agent Core and why should we care?

[00:00:25]So, here's the reality. You have probably built a cool AI agent or chatbot prototype and maybe it answers questions, calls few APIs, and does some reasoning. It runs beautifully in your notebook or local environment. But then someone ask, can we ship this to 10,000 users by next month?

[00:00:44]Now, here comes the challenge and suddenly you are staring down a mountain of infrastructure problems that have absolutely nothing to do with your agent's intelligence.

[00:00:53]That gap between a working prototype and a production grade system is exactly what Agent Core was built to bridge. In this session, I will walk you through every component of Agent Core, explain what each one does, and show you how they all fit together as a unified platform. By the end, you will have a complete mental model of the architecture and more importantly, you will be ready to start building AI agents in Agent Core.

[00:01:19]Let's get into it.

[00:01:22]So, for building a AI agent, you have amazing open-source framework, starts agent, LangGraph, CrewAI, LlamaIndex, and other frameworks. This makes it incredibly easy to build a working prototype. You decorate a function, write a system prompt, and boom, your agent reasons, uses tool, and solves your problem. That part is solved. But on the right is production.

[00:01:46]And in production, we have thousands of users hitting your agent simultaneously.

[00:01:50]Each user needs their own isolated session. Customer A's data cannot leak into customer B's conversation.

[00:01:57]The agent needs to remember things across days and weeks. It needs secure access of third-party services without hard coding API keys.

[00:02:06]You need to trace every decision, every tool call, every response.

[00:02:10]And you need guardrails to prevent dangerous actions.

[00:02:14]Developers end up spending months building this infrastructure.

[00:02:18]Whether building a session management, custom memory store, hand rolled with off flows, monitoring dashboard, all undifferentiated heavy lifting.

[00:02:27]And as the ecosystem evolves with new protocols like MCP and A2A protocols, your infrastructure needs to keep up.

[00:02:35]And that's the gap Agent Core fills.

[00:02:38]So, what is Agent Core in practical? So, it's an enterprise grade suit for fully managed service designed to accelerate moving your agentic applications from proof of concept to production. It's called It's not a framework, it's an It works with your framework. It's It's not a model, but it works with any model you supply. It's the infrastructure layer between your agent's brain and the real world applications.

[00:03:05]So, there are three components that drive this.

[00:03:08]The first one is faster time to value.

[00:03:10]Instead of months building this infrastructure, you use managed services that just work. Focus on your agent's logic. Agent Core handles the rest.

[00:03:19]Second, the flexibility and choice. This is huge. When we use start agent, LangGraph, or any other framework, whatever framework you prefer, or use any model, Claude, Noa, Lama, Mistral, these services are composable.

[00:03:33]Use any of them, pick whatever you want, and it works in Agent Core.

[00:03:38]Third, security and trust. Enterprise grade isolation, comprehensive access control, your agent operates reliably at scale, and you can prove it to your compliance team. So, Agent Core handles all your security and trust from its point.

[00:03:54]So, Agent Core also has different components through which it achieves all these things.

[00:03:59]Uh some of them are Agent Core runtime, Agent Core identity, Agent Core gateway, Agent Core policy, Agent Core interpreter, and then Agent Core browser, memory, observability, and evaluations.

[00:04:12]And think of it as a toolkit which each service solves a specific production problem, and together they become Agent Core.

[00:04:23]Okay, let's jump into the first one, which is Agent Core runtime.

[00:04:27]Agent Core runtime is a secure serverless component compute environment purposely built for AI agents.

[00:04:34]So, you will be thinking why not use AWS Lambda? So, agents are fundamentally different from typical web services.

[00:04:41]Agents Agent conversation run for minutes and not milliseconds. They may need extended runtime.

[00:04:49]Each session needs true isolation. They have a different sandbox for each user that is having an interaction.

[00:04:56]Agents handle multimodal payloads. Your agent can be dealing with text, images, files, tool outputs, anything, and in just one session.

[00:05:07]Then we have something called as cold starts.

[00:05:10]Because this is more of a agent or a user working in a real time, we need that the agent should reply in real time rather than waiting or having a cold start problem.

[00:05:21]So, Agent Core runtime solve all of this problem by providing you the auto scaling to match the demand, fixing your fast fast cold start, then completing your session isolation, built identity integration, and support for MCP and E2E protocols. The key idea, you write your agent code, package it, and deploy it in runtime. And it scales from zero to thousands of concurrent users without managing a single server.

[00:05:48]Next is Agent Core Identity. Your agent needs to act on behalf of users.

[00:05:53]It can check calendars, read emails, update CRM dashboards, or databases. But how do you make sure that it does securely?

[00:06:03]You can't hardcode API keys because it's a security disaster. And you can't ask users to paste credential. It's a terrible user experience. And you cannot give blanket access because that will violate your least privilege principles.

[00:06:16]So, Agent Core Identity provides three things.

[00:06:18]Compatible with existing providers.

[00:06:21]So, it works with whatever authentication system you already have, and there is no migration required to deploy an Agent Core Identity.

[00:06:29]Second is secure vault secure token vault. So, it stores and manages OAuth tokens securely, and users can authorize once the agent acts within defined boundaries.

[00:06:40]Third, just enough access. Agents only access what they need, nothing more.

[00:06:46]So, when you when your agent books a meeting on someone's calendar, Identity handles the entire auth flow, stores the token, provide it at runtime, and does the task for you.

[00:06:58]This is what makes Agent truly useful in enterprise settings. They can actually do things, not just talk about them.

[00:07:09]Okay, next is Agent Core Gateway. This is where your agent gets superpower at scale.

[00:07:14]In a program type, you define tools as Python functions, and works fine for a couple of tools, but in production, you have hundreds of tools, and multiple agents may require to share with them.

[00:07:25]So, Gateway solves three things.

[00:07:27]First is tool transformation. You have existing APIs or or lambda functions, and Gateway transforms them into agent compatible tools. There is no rewriting and no adapters. Next is tool discovery.

[00:07:40]This is really powerful. Gateway has semantic search built in. Your agent doesn't need to know every tool up front. It searches for the right tool based on the task. And for example, you get 1,000 tools, the agent finds the relevant three or four tools, and and keeps the prompt and the latency low.

[00:07:59]Third is shared access. Multiple agents use the same tool throughout through Gateway. Define one and you can share with your entire organization.

[00:08:07]Think of a Gateway as your organizational tool catalog.

[00:08:11]A centralized place where all capabilities are registered, discovered, and can be accessible securely.

[00:08:19]Next is agent core policy. This is your safety net or a firewall.

[00:08:24]Agents are powerful, but power needs boundaries. What if your agent decides to delete production database or send an email to the CEO on behalf of user?

[00:08:35]Access or access customer data it shouldn't.

[00:08:39]You write policies using a Cedar, which is an open-source authorization language, or you can write it in a natural language prompts. All agent traffic through Gateway gets interpreted by agent policy by policy engine.

[00:08:52]So, every request is evaluated against your policy and then the tool can access it. If it violates your policy, it's blocked. And it's blocked deterministically every time.

[00:09:04]And the critical word is deterministic.

[00:09:07]Unlike guardrails in a prompt, which is which the model might ignore, policies are enforced outside the agent core.

[00:09:15]The agent can't reason its way around it. It's like a firewall for your agent's action. This is what gives enterprise confidence to deploy agents in sensitive environments.

[00:09:28]Okay, next is code interpreter and browser. Agent core provides two powerful building tools.

[00:09:35]And this are code interpreter and browser runtime. So, what is code interpreter? This enables agent to write and execute code in an isolated environment. For example, you need to analyze a CSV, generate a chart, or run some calculation. Code interpreter provides a secure environment where your code runs without any risk to production.

[00:09:58]Each execution is fully isolated from the other.

[00:10:02]Next, the other is browser runtime, a cloud-based headless browser that lets agent interact with websites at scale.

[00:10:10]It can fill forms for you, scrap information, navigate web application, all in a fast and secure environment.

[00:10:17]Now, this both are fully managed. There is no infrastructure to provision, no container to manage, and it's automatically scales.

[00:10:25]They are enterprise-grade capability you would otherwise spend weeks building yourself.

[00:10:30]Think of them as a pre-built superpowers that you can hand it to your agent, regardless of framework that you are using.

[00:10:39]Okay, next is agent core memory. This is persistence that transform agent from stateless responders to intelligent assistance.

[00:10:48]For example, without memory, every conversation will start from zero. Your agent doesn't know the customer called yesterday about the same issue, doesn't know they prefer email over phone, doesn't know they are a VIP customer who has been with you for more than 10 years.

[00:11:04]Agent core memory provides two types, short-term memory and long-term memory.

[00:11:09]Short-term memory is when your conversation has context within the same session.

[00:11:14]Memory is managed in a long conversation, but it's not overflow it doesn't overflow the context windows and neither degrades the quality.

[00:11:23]While long-term memory is a game-changer.

[00:11:26]In the information that persist across different sessions can be preserved in long-term memory. So, customer preferences, past interaction, personal details, and most powerful tool is this long-term memory can be shared across different agent. So, your support agent can have a memory which is shared with your sales agent, onboarding agent, or analytics team as well.

[00:11:52]Next is Agent Core Observability.

[00:11:55]So, you can't improve when you can't see.

[00:11:59]In production, your agent handles hundreds of conversations simultaneously.

[00:12:03]How do you know it's performing well?

[00:12:06]How do you debug when someone says your bot gave me wrong information?

[00:12:10]So, this is solved using Agent Core Observability.

[00:12:13]So, first is tracing.

[00:12:15]Every step is captured in tracing. Which tool was called, what parameters were passed, and what the response was.

[00:12:22]And how long it took to process this response. Like a flight recorder for every conversation.

[00:12:28]Next is debugging. When someone goes something goes wrong, you can replay the exact sequence, see what the agent reasoned, decided, and where it went wrong.

[00:12:40]Third is monitoring.

[00:12:41]So, it provides a unified monitoring dashboard showcasing performance based on latency, error rate, tool usage, and other metrics as well.

[00:12:51]And it's built on top of Open Telemetry, that is the open standard, so it can be integrated with whatever monitoring tools you already have.

[00:13:01]Next is Agent Core Evaluations. So, Agent Core Evaluation answers the biggest question project manager has.

[00:13:09]How good is your agent?

[00:13:11]And to answer this, we have multiple metrics.

[00:13:14]So, first is helpfulness.

[00:13:16]Did the agent actually solve your problem?

[00:13:20]Next, correctness.

[00:13:21]Was the information accurate enough?

[00:13:24]Third, goal success rate. How often does your agent complete the intended task?

[00:13:30]So, it comes with built-in evaluators for common metrics, and you can add your custom evaluators, and you can also add LLM as a judge technique where every model where another model will evaluate your agent responses.

[00:13:45]And the really powerful feature is online evaluation, which is continuously monitoring your live traffic with configurable samples.

[00:13:53]And instead of testing occasionally, you have a constant constant pulse of quality. If your agent starts degrading, you know immediately.

[00:14:07]Okay, now let's zoom out. How it all fits together.

[00:14:11]I will go through end-to-end life cycle through agent core components.

[00:14:16]For example, a customer opens your app and types a message. So, runtime receives this request, spins up an isolated session.

[00:14:25]Now, it it calls agent core identity, which checks who this user is and what kind of access they have.

[00:14:34]Then, it has agent core memory, which retrieve their history, past issues, preferences, account details, etc. Now, the agent reasons about the question that has been asked and decides if it needs more information.

[00:14:49]And then, if it requires more information, it goes to agent core gateway, which discovers the right tool and policy agent core policy intercepts and verify is this agent allowed it to access this customer's data or not.

[00:15:04]If yes, it will access the data, and the agent gets the information and formulates a response.

[00:15:11]Now, memory stores new information from this interaction.

[00:15:15]And then, observability captures the entire trace, every step that it took to come up at this uh response.

[00:15:24]And evaluator samples this conversation and score it for later quality purposes.

[00:15:29]So, all this is happening in seconds.

[00:15:32]And you, the developer, wrote the agent logic and system prompt. Agent Core handled everything else and completed the task in seconds.

[00:15:41]And that is the power of going from prototype to production with Agent Core.

[00:15:53]So, if you need more information related to Agent Core, you can go through this website Agent Core on AWS. And we also have workshop that is very relevant to Agent Core getting started with Agent Core. And if you want new updates or feature released by Agent Core, you can follow this particular link.

[00:16:13]Yep. And if you enjoyed this, connect me on LinkedIn and follow BSA on YouTube, Instagram, Facebook, LinkedIn, or Discord, whatever platform works best for you. Pick your favorite, stay in the loop, and I will see you soon. Thank you.