Managing LLM Attention in Extended Agent Workflows

One of the biggest challenges I see with scaling LLM agents isn’t the model itself. It’s context. Agents break down not because they “can’t think” but because they lose track of what’s happened, what’s been decided, and why. Here’s the pattern I notice: 👉 For short tasks, things work fine. The agent remembers the conversation so far, does its subtasks, and pulls everything together reliably. 👉 But the moment the task gets longer, the context window fills up, and the agent starts forgetting key decisions. That’s when results become inconsistent, and trust breaks down. That’s where Context Engineering comes in. 🔑 Principle 1: Share Full Context, Not Just Results Reliability starts with transparency. If an agent only shares the final outputs of subtasks, the decision-making trail is lost. That makes it impossible to debug or reproduce. You need the full trace, not just the answer. 🔑 Principle 2: Every Action Is an Implicit Decision Every step in a workflow isn’t just “doing the work”, it’s making a decision. And if those decisions conflict because context was lost along the way, you end up with unreliable results. ✨ The Solution to this is "Engineer Smarter Context" It’s not about dumping more history into the next step. It’s about carrying forward the right pieces of context: → Summarize the messy details into something digestible. → Keep the key decisions and turning points visible. → Drop the noise that doesn’t matter. When you do this well, agents can finally handle longer, more complex workflows without falling apart. Reliability doesn’t come from bigger context windows. It comes from smarter context windows. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

LLMs are great at many things; however, continuous decision-making, which is needed for agentic work, is not one of them! A team of researchers has developed SAGE (Self-evolving Agents with Reflective and Memory-augmented Abilities), an innovative framework to enhance large language models' decision-making capabilities in complex, dynamic environments. The backbone of SAGE consists of three main components: - Iterative Feedback Mechanism - Reflection Module - Memory Management System Iterative Feedback Mechanism The Iterative Feedback Mechanism involves three key agents: - User (U): Initiates tasks and provides initial input. - Assistant (A): Generates text and actions based on environmental observations. - Checker (C): Evaluates the assistant's output and provides feedback. The iterative process continues until the checker deems the assistant's output correct or the iteration limit is reached. This mechanism allows for continuous improvement of the assistant's responses. Reflection Module The Reflection Module enables the assistant to analyze past experiences and store learned lessons in memory. It provides a sparse reward signal, such as binary success states, and generates self-reflections. These reflections are more informative than scalar rewards and are stored in the agent's memory for future reference. Memory Management System SAGE employs a sophisticated memory management system divided into two types: - Short-Term Memory (STM): Stores immediately relevant information for the current task. It's highly volatile and frequently updated. - Long-Term Memory (LTM): Retains information deemed important for future tasks. It has a larger capacity and can store information for extended periods. A key innovation in SAGE is also the MemorySyntax method, which combines the Ebbinghaus forgetting curve with linguistic knowledge. This approach optimizes the agent's memory and external storage management by: - Adjusting sentence structure based on part-of-speech priority. - Simulating human memory and forgetting mechanisms. - Managing the transfer of information between working memory (Ms) and long-term memory (Ml).

"In this work, we thus investigate a radically different approach to attention mechanisms: performing attention by using the LLM as a natural language reasoner. Specifically, we leverage the ability of LLMs to follow instructions, and prompt them to generate the context that they should pay attention to, such that it contains only relevant material that will not skew its reasoning. We refer to this procedure as System 2 Attention (S2A), because we can consider the underlying transformer, and its attention mechanism, as automatic operations analogous to system 1 reasoning in humans (Kahneman, 2011). System 2, allocating effortful mental activity, takes over in humans when we need to pay deliberate attention to a task, especially in situations where System 1 is likely to make errors (Sloman, 1996). This subsystem is hence similar to the goal of our S2A approach, as our aim is to alleviate the aforementioned failures of transformer soft attention with extra deliberate effort from the reasoning engine (LLM)": https://lnkd.in/gWm7ksib #LLMs

There has been a shift from prompt engineering to context engineering, where it is no longer about just crafting perfect prompts but also building systems that manage information flow over time. Yes, there is so much noise about context engineering lately; let's demystify and understand what exactly it is. Context engineering is the art and science of managing the information an LLM uses to think, act, and decide. Just as operating systems manage what data enters a CPU’s RAM, developers must now determine what goes into an LLM’s limited context window. With agents, this challenge becomes even more critical — and more complex. Coined by AI practitioners and emphasized by researchers at Anthropic and Cognition, context engineering refers to the strategic packaging and management of data presented to an LLM at any given point during its operation. This includes: → Prompts: User instructions or system messages. → Retrieved content: Documents or facts pulled in real-time. → Tool outputs: Responses from APIs or function calls. Andrej Karpathy likened LLMs to a new kind of CPU, where the context window functions like RAM. Because this “working memory” is finite — often capped at tens or hundreds of thousands of tokens — effective context engineering is essential for performance, cost-efficiency, and task success. Why Context Engineering Matters for Agents AI agents, unlike simple chatbots, operate across long sessions, multiple tool calls, and dynamic workflows. They’re designed to: → Make decisions → Execute plans → Persist knowledge → Collaborate with other agents Each of these functions requires careful context management. Left unchecked, context windows become bloated, tool outputs consume resources, and the model’s performance degrades — a phenomenon known as context degradation syndrome. Know more about context engineering in this wonderful article by Ankush Singal: https://lnkd.in/gT7ZFjZ2 Follow my Youtube channel for more AI related hands-on videos: https://lnkd.in/gmAKSxKJ

Good paper summarizing Context Engineering. The paper is 166 pages but only 60 pages content, rest references Context Engineering is a formal discipline focused on the systematic optimization of information payloads for Large Language Models (LLMs) during inference. It moves beyond simple prompt design by treating the input context (C) not as a static string, but as a dynamically structured set of informational components that are sourced, filtered, formatted, and orchestrated. The field is broken down into two main categories: 1) Foundational Components: These are the core technical capabilities for handling context:     a) Context Retrieval and Generation: This involves creating effective instructions (prompt-based generation) and acquiring external knowledge from various sources. Techniques include prompt engineering and external knowledge retrieval, such as from knowledge graphs.     b) Context Processing: This component focuses on transforming and optimizing acquired information. It deals with handling long sequences, enabling LLMs to refine their own outputs, and integrating structured and multimodal information.     c) Context Management: This addresses the efficient organization, storage, and utilization of contextual information, including managing memory hierarchies, applying compression techniques, and working within context window constraints. 2) System Implementations: These are architectural integrations of the foundational components to create sophisticated AI systems:     a) Retrieval-Augmented Generation (RAG): Combines LLMs' internal knowledge with external retrieved information.     b) Memory Systems: Enable persistent interactions and allow LLMs to maintain state across conversations, overcoming their inherent statelessness.     c) Tool-Integrated Reasoning: Allows LLMs to use external tools for function calling and interacting with environments, addressing limitations like outdated knowledge or calculation inaccuracy.     d) Multi-Agent Systems: Involve coordinating communication and orchestration among multiple LLM agents. The purpose of Context Engineering is to enhance LLM performance, optimize resource usage, and unlock future potential for LLM applications. It is essential because while LLMs are proficient at understanding complex contexts when augmented by advanced context engineering, they still face challenges, particularly in generating equally sophisticated, long-form outputs. The discipline helps mitigate issues like hallucinations, unfaithfulness to input, and sensitivity to input variations. It shifts the focus from the "art" of prompt design to the "science" of information logistics and system optimization. Think of Context Engineering as an advanced AI operating system for LLMs. Just as an operating system manages a computer's memory, processes, and external devices to run applications efficiently