Event 1

Event Report “Agentic AI Build Week Demo Day”

Purpose of the Event

  • Explore innovative solutions combining the power of Generative AI and Agentic AI (especially Amazon Bedrock and AgentCore) to solve real-world problems.
  • Learn best practices in building automated AI systems on the AWS platform.
  • Connect and learn from the top teams about deploying Multi-Agent models, Computer Vision, and Serverless architectures.

List of Speakers (Team Representatives)

  • Team 3KA - Presented S.H.E.P.H.E.R.D (Crowd Management via Computer Vision)
  • Team OneTeam - Presented AI-Powered Conversation Ordering (KFC Bot Agent)
  • Team Plan V - Presented Solution Architect Professional Native App
  • Dream AI Team - Presented Signal Scout (Corporate Strategy Signal Analysis)

Key Highlights

S.H.E.P.H.E.R.D (Team 3KA)

  • Problem solved: Real-time crowd management, replacing manual monitoring with automated congestion detection and alerts.
  • Core Technology: Combined YOLO + ByteTrack for Computer Vision.
  • Agentic Layer: Used Autonomous Monitor for continuous supervision and Operator Copilot to allow operational staff to query via natural language using Amazon Bedrock AgentCore.

KFC Bot Agent (Team OneTeam)

  • Problem solved: Reduced drop-off rates by eliminating the need for customers to download a new app, letting them order directly within Zalo/WhatsApp chat frames.
  • Operational Flow: Applied a “Goal -> Plan -> Tools -> Act -> Verify” loop. The Bot automatically checks carts and applies promotions through OpenSearch and DynamoDB.
  • Cost optimization: Latency of just 3-5 seconds, with incredibly low costs (~$0.006/order) thanks to a Serverless AgentCore architecture.

SA Professional AI Native App (Team Plan V)

  • Problem solved: Automated architecture design tasks, requirement extraction, and cost estimation for Solution Architects.
  • Supported Tools: Integrated AWS Pricing MCP and Draw.io MCP to draw visual diagrams and provide projected costs directly during chats.

Signal Scout (Dream AI Team)

  • Problem solved: Automated competitor data collection and analysis rather than scattered manual research.
  • Multi-Agent Architecture: A Crawler Subagent gathers data (via Apify, LangFuse), then an Analysis Subagent processes it, assesses risks, and provides visual report dashboards for management.

What I Learned

Design Thinking

  • Agentic AI superseding pure Generative AI: AI no longer stops at text generation but has advanced to automatic Tool Use and Action decision-making.
  • Design Once | Deploy Everywhere: Optimizing the core architecture to plug into various chat channels (Zalo, Messenger) without having to rebuild from scratch.

Technical Architecture

  • Serverless & Microservices: Fully utilizing ECS Fargate, Lambda, and DynamoDB to ensure the system can auto-scale at the lowest possible cost.
  • Model Context Protocol (MCP) Integration: Using MCPs to provide context and intervention rights to external APIs for the Agent.
  • Context Management (Memory Layer): Separating Short-Term Memory and Long-Term Memory (vector databases like OpenSearch) to maintain seamless context in chat flows.

Modernization Strategy

  • Automating AI Routing: Applying Multi-Agent systems to break down complex problems into Subagents responsible for specialized stages (Crawler, Analyzer, Reporter).
  • Focusing on Cost-efficiency: AWS aims to build architectures that are not only powerful but also cheap and optimized (e.g., the KFC Bot project costs only $0.006/request).

Application to Work

  • Applying AgentCore to chatbot projects: Upgrading current chatbot systems from Rule-based to Agentic-based so chatbots can automatically look up internal databases.
  • Using MCP in programming: Integrating AWS Pricing MCP to quickly provide cost estimates during architecture proposals for clients.
  • Improving attendance processes (Project BK-Sync): Further researching Computer Vision to potentially identify student faces automatically alongside QR codes in the future.

Event Experience

Attending the Agentic AI Build Week Demo Day was an eye-opening experience, demonstrating the high practicality of AI when applied to Enterprise problems.

Learning from experts and young engineers

  • Despite the short Hackathon duration (typically 24h - 48h), teams created complete products with incredibly impressive latency and costs.

Practical Technical Experience

  • Understood the design lifecycle of an Agentic System from the Ingestion Layer -> AgentCore Runtime -> Tool Layer -> Data & Assets Layer.
  • Realized the power of combining AWS Bedrock with built-in security services (WAF, Cognito) to create safe AI systems for enterprises.

Key Takeaways

  • Preparation (A clear goal, ready toolkit, defined roles) is the deciding factor in building products rapidly.
  • Today’s AI systems must not only be smart but also “explainable” and “actionable”.

Event Photos

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