Deploying AI Agents on AWS | Bedrock AgentCore, MCP, Human-in-the-Loop & Amazon Quick

In this video, I am learning what happens after an AI agent is already built. Creating the agent logic is only the beginning. A real application also needs a safe place to run the agent, memory to keep useful context, secure access to tools, monitoring, identity, policies, and sometimes human review. That is where Amazon Bedrock AgentCore becomes important. One simple way I understood this topic is: Agent framework builds the agent. AgentCore helps operate the agent. Amazon Bedrock AgentCore can work with different agent frameworks such as Strands, LangGraph, LangChain, CrewAI, and other agent SDKs. It provides managed capabilities around the agent so developers do not have to build every infrastructure component from the beginning. One important part is AgentCore Runtime. Runtime gives the agent a managed serverless place to run. A simple flow is: Application → AgentCore Runtime → AI Agent → Tools / Knowledge → Response This means the developer can focus more on the agent behavior instead of manually building the full hosting platform. I also learned about AgentCore Memory. Short-term memory helps the agent remember what is happening in the current conversation. Long-term memory can keep useful information from earlier sessions, such as preferences, facts, or summaries. For example: A user says: “I prefer short bullet-point reports.” Later, the agent can remember that preference and format future reports in the same way. Another useful part is AgentCore Gateway. An AI agent may need to connect to many APIs, Lambda functions, or business services. Simple flow: Agent → AgentCore Gateway → API / Lambda / Business Service Security is also very important when agents can take real actions. AgentCore Identity helps manage the identity used by the agent, while policies can control what the agent is allowed to do. For example, a support agent may be allowed to read an order status but may not be allowed to issue a large refund. The main idea is: Give the agent only the permissions it actually needs. I also learned about monitoring and evaluation. An agent may work well during testing but behave differently when real users start using it. Monitoring can help find problems such as: • Slow responses • Failed tool calls • Unexpected behavior • Poor answer quality This is why observability is important after deployment. Another topic in this video is Model Context Protocol, or MCP. MCP provides a more standard way for AI applications to connect with external tools and information sources. Instead of creating a completely different integration for every service, MCP gives applications a common way to discover and use tools. For example, an agent could use an MCP connection to create a GitHub issue, work with Slack, connect to PostgreSQL, or use another external platform. I also cover Human-in-the-Loop. Not every decision should be fully automatic. Some tasks may involve high risk, missing information, or sensitive business decisions. A simple flow is: AI Agent → Confidence / Risk Check → Automatic Path OR Human Review Toward the end, I explain Amazon Quick. Amazon Quick brings Agentic AI closer to business users. For example: “Summarize this month’s customer complaints and create a follow-up task for the most common issue.” Quick can search the connected data, identify the common issue, prepare the summary, and then use a connected action to create the task. The biggest lesson I learned from this topic is: A useful AI agent needs more than a Foundation Model. I would like to thank Dr. Victor Govindaswami for helping us understand the operational side of Agentic AI. This topic helped me see the difference between simply building an agent and actually running that agent in a real environment where security, memory, monitoring, and permissions all matter. I also appreciate how he continues to connect each new topic with the concepts we learned before. Studying AgentCore, MCP, human oversight, and Amazon Quick helped me understand how agent design can move into a complete real-world AI system. I am thankful for the guidance and support he continues to provide throughout this learning journey. GitHub Repository: https://github.com/Aka-sh20/AWS-Certi... LinkedIn Post: https://www.linkedin.com/posts/patel-... Thank you for watching. Please Like, Share, and Subscribe if this video was helpful. Presented by: Akash Patel Concordia University Chicago #AWS #AmazonBedrock #AgentCore #BedrockAgentCore #AgenticAI #AIAgents #MCP #ModelContextProtocol #HumanInTheLoop #AmazonQuick #AgentMemory #AgentGateway #AIObservability #CloudSecurity #GenerativeAI #AWSGenerativeAI #AIEngineering #AWSCloud #AWSDeveloper #AWSCertification #LearnAWS #ComputerScience