André Lindenberg’s Post

One of the biggest challenges to high Agentic performance has always been maintaining context integrity. Every iteration of prompt optimization or memory rewriting seems to make the agent forget what made it smart in the first place. The result is what researchers now call brevity bias and context collapse, where iterative rewriting simplifies prompts to the point that the system loses all nuance and depth. ACE (Agentic Context Engineering) eliminates the need to overwrite context while allowing it to evolve. It treats context as a living playbook that grows richer with every interaction. Each update is modular and incremental, allowing agents to: 🔹Keep learning strategies instead of discarding them 🔹Retain domain heuristics essential for multi-turn reasoning and tool use 🔹Adapt faster while reducing latency and rollout costs significantly To make this possible, ACE’s architecture introduces three intelligent components that mirror how we humans improve with experience: 🧠 Generator creates reasoning paths 🔍 Reflector learns from outcomes 📚 Curator organizes those lessons into a structured and reusable memory As a result, you have an agent that learns continuously without retraining. Check out benchmarks such as AppWorld and FiNER in the below paper, where ACE delivered double digit accuracy gains while reducing adaptation latency by more than eighty percent. From a product and systems perspective, ACE goes beyond an optimization framework to redefine how agents evolve. Context becomes the foundation for learning itself, making agents more performant, transparent, explainable, and self improving with every decision cycle. #AIAgents #AgentContextEngineering

At www.jaiinfoway.com we explore the engineering brilliance behind Large Language Models and innovations like Grouped-Query Attention Multi-Head Latent Attention and Sliding-Window Attention. These optimizations make AI faster and more efficient while maintaining accuracy across massive datasets. From memory-efficient architectures to intelligent context handling our team at www.jaiinfoway.com integrates these advancements into scalable enterprise AI systems that drive real-world impact. We’re committed to turning complex AI research into practical solutions for businesses worldwide. #Jaiinfoway #AI #LLM #DeepLearning #Innovation #MachineLearning #ArtificialIntelligence #FutureTech #AIResearch #DigitalTransformation

𝗡𝗲𝘄 𝗯𝘂𝗶𝗹𝗱: 𝗶𝗻𝘀𝘁𝗮𝗻𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗳𝗼𝗿 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸 𝗣𝗮𝗴𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 (𝗻𝟴𝗻) 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀 When someone DMs the page about price, stock or delivery, the workflow replies in seconds using the answers I’ve approved. If the question is unusual, it hands off to a human. 𝗛𝗼𝘄 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁 - Listen: Webhook catches new messages. - Clean: I pull just what I need (sender, text, message ID) and ignore duplicates. - Answer: An AI step checks a 𝗚𝗼𝗼𝗴𝗹𝗲 𝗦𝗵𝗲𝗲𝘁 with FAQs/products and drafts a short, friendly reply. - Send: The response goes back to Messenger. - Log: I keep a simple record so follow-ups are easy. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 (𝘄𝗵𝗮𝘁 𝘄𝗮𝘀 𝗶𝗻 𝗺𝘆 𝗵𝗲𝗮𝗱) 1. Fast first reply: most DMs are simple—answer them instantly, escalate the rest. 2. No guessing: only answer from the sheet; otherwise ask a quick clarifying question or route to a teammate. 3. Easy to maintain: update the sheet to change answers—no redeploy. 4. Respectful by default: concise messages, no emojis, on-brand wording. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 - Skip duplicate events (message ID check). - Short timeouts + a polite fallback if something fails. - Conversation log for QA and learning. 𝗡𝗲𝘅𝘁 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Attachments, product links → ID detection, and a tiny console to view unresolved threads.

𝐒𝐋𝐌 𝐯𝐬 𝐋𝐋𝐌: 𝐖𝐡𝐢𝐜𝐡 𝐢𝐬 𝐫𝐢𝐠𝐡𝐭 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬? (Small Language Models (SLM) vs. Large Language Models (LLM)) Our new whitepaper breaks it down, covering: • Real-world cost-benefit analysis • Smart routing strategies • Hybrid approaches to maximize ROI • Industry-specific use cases Stop guessing, start strategizing. 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐧𝐨𝐰- https://lnkd.in/gtavRNuE #SLMLLM #whitepaper #strategy

Glad for the opportunity to contribute to this Whitepaper on SLM | LLM. Along the way, we tested and compared outputs, explored performance and cost trade-offs, and shared insights on when LLMs are truly efficient and when SLMs work just fine.

🧠 Agent Memory that Scales: Short-Term vs Long-Term (Semantic / Episodic / Procedural) • Reality check: LLMs are stateless. Any “memory” is an engineering layer you add. • Memory model: • 🗣️ Short-term → session context, tool outputs, scratchpad. • 🧾 Long-term → • Semantic: facts/preferences (user profile, org policies). • Episodic: past tasks/interactions with outcomes. • Procedural: how the agent executes routines (playbooks/skills). • Why it matters: Improves personalization, continuity, and continual learning without retraining. • Implementation sketch (e.g., Zep): • Graph store for entities/events; embed + link conversations, docs, tool runs. • Memory policies: TTL, PII redaction, summarization at thresholds (token/age). • Quality gates: Retrieval eval (hit rate, precision@k), hallucination checks vs stored facts. • Pro tips: Separate read/write paths, version memories, and surface citations in responses to build user trust. #dailydoseofds #datascience #machinelearning #MLOps #AIagents #Memory #KnowledgeGraphs #ContinualLearning

Is ACE the next big leap in Context Engineering? Meet ACE (Agentic Context Engineering) — a new framework that outperforms current state-of-the-art optimizers like GEPA, by treating context not as a static prompt but as a living, evolving knowledge space. 🔍 What makes ACE different? Instead of rewriting the entire prompt, ACE manages context as a structured collection of discrete knowledge items — strategies, code snippets, error handlers — that evolve and refine over time based on performance feedback. Where GEPA (Genetic-Pareto) focuses on optimizing for brevity and generalization, it often risks losing crucial heuristics — a phenomenon known as brevity bias or context collapse. In contrast, ACE emphasizes retaining deep domain insights, using non-LLM logic to manage context growth while preserving specialized knowledge and edge-case strategies. ⚙️ How ACE works 1️⃣ Generator → Attempts a task, producing reasoning traces and environment feedback. 2️⃣ Reflector → Analyzes outcomes to extract key insights or root causes of errors. 3️⃣ Curator → Converts insights into structured “delta” updates — small, meaningful additions or edits to the knowledge base. These deltas are then merged into the context, allowing it to grow incrementally and intelligently with each iteration. 📊 Results that matter 🔹 +10.6% improvement on agentic benchmarks. 🔹 +8.6% boost in complex financial reasoning. 🔹 86.9% reduction in adaptation latency vs. full-prompt rewrite methods. ACE doesn’t just optimize prompts — it builds a memory that learns, adapts, and evolves. 📄 Paper: https://lnkd.in/eBknvYcR #ACE #AgenticAI #ContextEngineering #PromptOptimization #LLMResearch #AIFramework #MachineLearning #AIInnovation #GEPA #PromptEngineering #AIResearch #AgenticSystems #GenerativeAI #DeepLearning Umar Iftikhar

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐭𝐫𝐚𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐋𝐋𝐌 𝐚𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠?? 🤖✨ Meet 𝐀𝐠𝐞𝐧𝐭 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐞𝐫 (𝐀𝐑𝐓) – an open-source framework that makes training multi-step, reliable LLM agents a breeze. ART uses 𝐆𝐑𝐏𝐎 (𝐆𝐫𝐨𝐮𝐩 𝐑𝐞𝐥𝐚𝐭𝐢𝐯𝐞 𝐏𝐨𝐥𝐢𝐜𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧) for building robust, real-world agents—without The headache of writing manual reward functions. Instead, it leverages 𝐑𝐔𝐋𝐄𝐑 (𝐑𝐞𝐥𝐚𝐭𝐢𝐯𝐞 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐋𝐋𝐌-𝐄𝐥𝐢𝐜𝐢𝐭𝐞𝐝 𝐑𝐞𝐰𝐚𝐫𝐝𝐬), an LLM-based evaluator that automatically assigns rewards, eliminating tedious hand-crafted reward engineering. Why 𝐀𝐑𝐓 is a game-changer: • ⚡ 2–3x faster development – skip reward function engineering entirely • 🧰 General-purpose – works across any task, no modification needed • 📈 Proven performance – matches or exceeds hand-crafted rewards in 3/4 benchmarks • 🧩 Easy integration – drop-in replacement for manual reward functions • 🤖 Model agnostic – works with Qwen, Llama, GPT-style LLMs, and more And the best part? It’s 100% open source. 📌 Link to the GitHub repo is in the comments! #ReinforcementLearning #LLMAgents #AITraining #OpenSourceAI #MachineLearning #AICommunity #ArtificialIntelligence

In the past year, coding agent models have changed a great deal, and they are becoming a powerful part of how software gets built. If you work at a company that writes or maintains code, you may soon be working with one of these agents yourself. This post will walk with you through three ideas: -1) How agentic tooling works as the way you interact with these models. -2) How to think about the model that serves as the “coding brain.” -3) How to look at benchmarks and leaderboards so you can make a fair comparison. EQengineered Author: Dakota Kim, Director of Engineering, Applied AI https://lnkd.in/g3RXwDqc

As a PM, AI saved me hours, but prompts made it priceless. I’ve learnt a lot about prompting, and honestly, it’s been one of the most transformative skills for me. In today’s world, using AI as a product manager is no longer optional, it’s essential. It helps you move faster, organize better, analyze smarter, and focus more on what truly matters: strategy and impact. But here’s the truth, using AI isn’t where it ends. Prompting is where the magic really happens. AI is only as powerful as the question you ask. And as a PM, good prompting can change the way you work, from analyzing user feedback and Uncovering key insights, to generating stakeholder-ready PRDs, research questions, and even helping you shape your next prototype or landing page copy. That’s why I use the RACE framework whenever I prompt: • R — Role: Who should AI be? (e.g., “Act as a senior PM…”) • A — Action: What do you want it to do? • C — Context: What details does it need? • E — Execution: What does a great output look like? RACE gets me cleaner PRDs, sharper research questions, and fast, reliable syntheses — the stuff you’d normally pay for in workshops or consulting. So I packaged my best, comprehensive battle-tested RACE prompts into a Google Doc and I’m giving it away for free. No gate, no catch. Grab the prompts + bias-checks + safety tips here: https://bit.ly/4oskCzx #Prompting# #ProductPrompts# #AIProductManager#