Presenters

Source

🚀 Level Up Your AI: Building Agentic Applications That Actually Deliver Value 💡

The buzz around AI agents is huge, but let’s be honest, a lot of AI pilot projects fall flat. A recent MIT study revealed a sobering statistic: only 5% of AI pilots actually deliver significant value. Why? According to Mickey, a Staff Developer Advocate, the culprit is often a lack of statefulness, personalization, learning, and memory – the very things that make applications truly useful.

This presentation wasn’t just about highlighting the problem; it was about showcasing a solution. Mickey walked us through a compelling demo of an agentic application built with some seriously cool tools, demonstrating how to build stateful applications with agent memory. Let’s dive in!

🛠️ The Tech Stack: Your Agentic Toolkit

The demo application showcased a powerful combination of technologies working together seamlessly:

  • Voyage, Atlas, Alice: These form the core, showcasing how they integrate for agentic workflows. Think of them as the building blocks for your agentic architecture.
  • Anthropic Models: Providing the brains – powering the agent’s reasoning and decision-making.
  • MongoDB: Acting as the memory bank, storing data and enabling persistent state.
  • Langchain: Handling text-to-query capabilities, making it easier to interact with your agent.
  • Multi-Agent Architecture: A clever design featuring a coordinator agent routing requests to specialized “pseudo agents” (thin wrapper libraries) and a robust MCP (Memory, Context, Processing) agent. This modularity allows for focused expertise.
  • Memory Patterns (Episodic, Semantic, Procedural): The application cleverly implements different memory types, managed by Atlas. This allows the agent to remember events, understand concepts, and follow workflows.
  • Hybrid Search (Full-Text & Semantic): Combining these search methods provides a powerful way to retrieve information effectively.
  • Sonnet: Optimizing context engineering through prompt caching and result streamlining – a key performance booster.
  • E2B: A potential platform for creating virtual sandboxes, allowing agents to self-refine and improve.

🎯 The “RAG is Dead” Myth & Context Engineering Challenges

One of the most important points Mickey made was to dispel the misconception that agents render Retrieval-Augmented Generation (RAG) obsolete. Agents and RAG are complementary! Agents build upon RAG, adding statefulness and intelligence.

However, building effective agentic applications isn’t without its challenges. Context engineering emerged as a significant hurdle. The key question is: when and what information should you feed your agent? Too much, and it gets overwhelmed. Too little, and it can’t make informed decisions.

Another trade-off to consider is balancing speed and accuracy. Direct slash commands can deliver results in milliseconds, but for deeper analysis, hybrid search (combining full-text and semantic search) provides a better balance, albeit at a slightly slower pace.

📊 Quantifiable Insights: Numbers That Matter

Let’s put some numbers to this:

  • 5% Success Rate: That’s the sobering statistic from the MIT study – only 5% of AI pilots deliver real value.
  • $20 vs. $20-50k: A simple $20 consumer app, thanks to statefulness and personalization, often outperforms significantly more expensive ($20-$50k) enterprise AI deployments. This highlights the power of a well-designed, stateful application.
  • 12 LM Calls: The demo application utilizes approximately 12 Language Model calls, demonstrating a reasonable level of complexity.

✨ Actionable Takeaways: Your Next Steps

Ready to put this knowledge into action? Here’s what Mickey encouraged attendees to do:

  • Fork and Improve! The demo project is open-source – fork it, play with it, and contribute your improvements!
  • Self-Refinement Agent Challenge: Consider building a self-refinement agent that analyzes query performance and automatically optimizes the application. This is a fantastic “homework” assignment to truly master the concepts.
  • Leverage Existing Frameworks: Don’t reinvent the wheel! Utilize existing agentic orchestration and memory frameworks like Mezzero and Agno to streamline development.
  • Embrace Hybrid Approaches: The future is likely a blend of different tools and patterns. Combine search, RAG, and agents to leverage their individual strengths and create truly powerful AI solutions.

🌐 Q&A: What the Audience Was Asking

The Q&A session highlighted the audience’s keen interest in context engineering within agentic applications. And a fun shout-out went to those who recognized the anime references sprinkled throughout the sample data! 👾

The bottom line? Building successful AI agents requires more than just throwing powerful models at a problem. It’s about thoughtful architecture, clever memory management, and a willingness to experiment. 🚀 Let’s move beyond those 5% failure rates and build AI applications that actually deliver value! 🦾

Appendix