Why AI Agents Fail: 3 Failure Modes That Cost You Tokens and Time

AI agents fail in predictable, expensive ways — and most Python developers don't discover them until production. This talk dissects three research-validated failure modes that cause agents to waste tokens, get stuck, and run up costs, with live demos and concrete Python fixes for each.

The three failure modes:

Context Window Overflow — Large tool outputs (214KB of logs) flood the context window and crash the agent. Fix: a Memory Pointer Pattern that reduces token usage by 7x (validated against IBM Research).

MCP Tools Not Responding — Slow external APIs cause 424 errors and block your agent indefinitely. Fix: an async handleId pattern that drops response time from 17.2s to 1.7s (validated against Octopus research).

Reasoning Loops — Agents call the same tool repeatedly with identical parameters without making progress. Fix: a Debounce Hook that detects and blocks duplicate calls in a sliding window (validated against The Decoder).

Each failure mode follows a "problem → research → live demo → solution" arc. All demos use open-source Python tools (Strands Agents, FastMCP, asyncio) and are available as runnable notebooks in a companion repository. The patterns are framework-agnostic — attendees can apply them to LangChain, CrewAI, AutoGen, or custom agent builds.

Aimed at intermediate-to-advanced Python developers building or evaluating AI agent systems who want to prevent the most common runtime failures before they hit production.

Solving AI Context Window Overflow: Implementing the Memory Pointer Pattern for Token-Bounded Agent

When agentic AI systems invoke external tools, the returned payloads frequently exceed the model's context window token limit — causing silent truncation, hallucinated completions, and non-deterministic agent behavior with no explicit error propagation. This session dissects the root cause of context window overflow in tool-augmented LLM pipelines and presents the Memory Pointer Pattern: an architectural fix that decouples data retrieval from context injection by persisting tool outputs to external object storage or key-value stores and passing typed memory pointers — lightweight reference objects containing URIs, schemas, and metadata — back into the agent's context frame. We'll walk through the implementation covering pointer serialization, lazy dereferencing strategies, token budget allocation algorithms, and graceful degradation when payloads exceed defined thresholds. Attendees will leave with concrete, cloud-agnostic patterns to eliminate silent context overflow failures in production agent systems.

Multi AI Agent collaboration patterns

Discover how to build sophisticated AI systems that leverage multiple specialized agents working together to solve complex problems. This technical session explores four powerful multi-agent collaboration patterns—Agents as Tools, Swarms, Agent Graphs, and Workflows—and demonstrates how to implement them using the open-source Strands Agents SDK with Amazon Nova foundation models.

Building Production-Ready AI Agents with Amazon Bedrock AgentCore: From Concept to Deployment

Discover how to build and deploy AI agents leveraging comprehensive set of enterprise-grade services that help developers quickly and securely deploy and operate AI agents at scale using any framework and model, hosted on Amazon Bedrock or elsewhere.

Whether you're building a proof-of-concept or preparing for production, learn how to accelerate your time to market with just a few lines of code while maintaining enterprise-level security and scalability.

Introduction to Azure AI Agent Service

Azure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, extensible AI agents without needing to manage the underlying compute and storage resources.

This session is for software engineers who are looking to build the next generation of AI applications leveraging the power of AI agents.

We will review fundamentals such as what an agent is, why to use Azure AI Agent Service, and how to start building AI agent solutions with Azure AI Foundry.

Retrieval Augmented Generation (RAG) power by Azure AI Search

Retrieval Augmentation Generation (RAG) is a sophisticated system designed to augment the capabilities of a Large Language Model (LLM), such as ChatGPT. This enhancement is achieved by integrating an information retrieval system that provides grounding data. This integration allows for precise control over the grounding data utilized by the LLM when formulating responses.
In an enterprise setting, the RAG architecture facilitates the confinement of generative AI to specific enterprise content. This content can be derived from vectorized documents, images, and other data formats, contingent upon the availability of embedding models for such content.
In this session, we will delve into the concept of RAG and learn how to implement a RAG architecture. This architecture encompasses an Application User Experience (App UX) in the form of a web application for user interaction, an Application Server or Orchestrator serving as the integration and coordination layer, Azure AI Search functioning as the information retrieval system, and Azure OpenAI acting as the LLM for generative AI. This comprehensive learning experience promises to equip attendees with a robust understanding of RAG and its practical implementation.

Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks

The future of AI is agentic. AI systems are evolving from having conversations to getting things done—this is where we expect much of AI’s value to shine. It’s the difference between generative AI recommending dinner options to agentic assistants that can autonomously place your order and arrange delivery.

This session is an introduction to understanding how Magnetic-One works, how it was built on top of the AutoGen framework, and how you can leverage the power of the AI Agent in your solutions.

Azure OpenAI patterns for software engineers

Azure OpenAI is a powerful service that enables developers to leverage the state-of-the-art language models from OpenAI to build intelligent applications. However, using Azure OpenAI effectively requires more than just calling the API. Developers need to understand the best practices and patterns for integrating Azure OpenAI into their solutions, as well as the limitations and challenges of working with natural language generation.

In this session, you will learn about the Azure OpenAI patterns, a set of guidelines and approaches to deliver common scenarios using Azure OpenAI. You will see how to apply these patterns to various use cases, such as. You will also learn how to optimize the precision and reliability of your Azure OpenAI applications, as well as how to handle errors, hallucinations, and user experience issues.

By the end of this session, you will have a solid understanding of how to use Azure OpenAI effectively and efficiently in your software engineering projects. You will also gain insights into the current and future capabilities of Azure OpenAI, as well as the ethical and social implications of natural language generation. Whether you are new to Azure OpenAI or already have some experience with it, this session will help you take your skills to the next level.

Enterprise Integration using Azure Serverless

Enterprise integration has been a challenge for long time, the enterprise organizations have deployed or built solution to solve their integration problems for their on-prem business application.
Now, in the new cloud age the enterprise integration challenge has mutated, solving in an easier way some traditional problems using cloud technologies like serverless but creating new challenges like Cloud to on-premise connectivity or end to end security.
On this session we will review some of the approach to use Microsoft Azure serverless services like Azure Functions and Logic Apps to solve enterprise integration problems, the experience to update some integration process based on Biztalk Server with Azure Logic Apps and some alternatives to connect process running on the cloud with applications and data on-premise. This session included process live demos integrating component in the cloud and on-premise.

Assistants API in Azure OpenAI

In this session, we will explore the API Assistants in Azure OpenAI Service. This new feature in Azure OpenAI Service, is now available in public preview. Assistants API represents a new cognitive architecture from OpenAI embedded in a product that makes it simple for developers to create high quality copilot-like experiences within their own applications. Previously, building custom AI assistants needed heavy lifting even for experienced developers. While the chat completions API is lightweight and powerful, it is inherently stateless, which means that developers had to manage conversation state and chat threads, tool integrations, retrieval documents and indexes, and execute code manually. Assistants API, as the stateful evolution of the chat completion API, provides a solution for these challenges.

Agentic AI: Unlocking the Power of Multi-Agent Systems

Dive into the dynamic world of Agentic AI with our expert-led session, “Unlocking the Power of Multi-Agent Systems.” In this comprehensive session, we’ll demystify the concept of agents within AutoGen—entities that can send and receive messages, generating replies using models, tools, human inputs, or a mixture of them. This multifaceted approach allows agents to mirror real-world and abstract entities, such as people and algorithms, streamlining complex workflows through agent collaboration.

Key Takeaways
Theoretical Foundations: Understand the core principles behind agents and their role in multi-agent systems.

Practical Demonstrations: Experience hands-on demos showcasing the practical implementation and collaboration of agents.

Extensibility and Composability: Learn how to extend simple agents with customizable components, creating modular workflows that are easy to maintain.

Real-World Applications: Discover how AutoGen is applied across various fields, from customer support and cybersecurity to finance and robotics.

Build your own Chat Copilot

In this interactive presentation designed for software engineers, you’ll learn how to construct your own intelligent Copilot chatbot. This session will delve into the utilization of Natural Language Processing and vector searching, among other capabilities. By harnessing the power of LLM-based AI, you can build the Copilot chatbot, enabling it to process and respond to information more effectively, including chat with your own data. To achieve this, we’re going to leverage the Semantic Kernel and its specific features that assist software engineers in quickly integrating AI capabilities into their solutions.

AI Agent & RAG Evaluation: Metrics, Tools

Building a Retrieval-Augmented Generation (RAG) system is only half the battle — how do you know it's actually working well? In this technical session, we'll demystify RAG evaluation from the ground up. You'll learn what makes RAG evaluation uniquely challenging, explore the key metrics that matter — including context relevance, faithfulness, correctness, completeness, and context coverage — and see how to put them into practice using Amazon Bedrock Evaluation and the open-source RAGAS library.
Through live demos, we'll walk through how LLM-as-a-judge technology works, how to bring your own datasets for tailored evaluations, and how to compare results across evaluation jobs to iteratively improve your RAG pipelines. Whether you're building with Amazon Bedrock Knowledge Bases or your own custom RAG stack, these techniques apply to any system hosted anywhere.

By the end of this session, you'll have a clear framework for measuring and improving the quality of your AI-powered search and answer generation systems — and you'll have seen it all in action through practical demonstrations.

Who Is Your Agent, Really? Fixing the Identity Anti-Patterns That Break Production Agentic AI

Most teams building agentic AI get identity dangerously wrong: one static service role, long-lived credentials shared across every agent, and an agent running as an all-powerful super-user — until it touches data it shouldn't, or a security review stops the project cold. In production, identity is where agentic AI quietly breaks.

This session dissects the most common identity anti-patterns in real agentic systems — the shared "god-mode" role, over-scoped credentials, no separation between agent and user identity, missing delegation boundaries, and audit trails that can't say which agent did what, on whose behalf. Then we show how modern agent identity platforms fix them: distinct workload identities, a secure credential vault enforcing least privilege, and identity-aware authorization that lets an agent act on behalf of an authenticated user with full context and auditability.

You'll leave with a clear model for agent identity and a checklist of anti-patterns to eliminate before they become incidents.

Level: Intermediate. Audience: Engineers, architects, and security/platform teams building or operating agentic AI. Prerequisites: Familiarity with AI agents and basic OAuth 2.0 / OIDC. Format: Technical session with architecture walkthroughs and demos. Views are the speaker's own; practices and standards are evolving.