context engineering
context engineering on Beyond Market Intelligence: a running collection of 10 stories we have gathered and hand-picked because they are worth your time. Every post here touches on context engineering in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around context engineering, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Presentation: Beyond Prompting: Context Engineering for Production-Grade AI
Ready to move beyond basic prompt engineering? Ricardo Ferreira’s presentation, “Beyond Prompting: Context Engineering for Production-Grade AI,” delivers practical architectural strategies for building robust AI applications. Ferreira explores critical techniques like leveraging Redis for memory management, optimizing token usage with summarization, and combating context rot through reranking and semantic caching—all while maintaining strict latency constraints and controlling API costs. For those navigating the complexities of LLM model naming, our guide, "A Complete Guide to Decoding LLM Model Names," offers valuable clarity.
Context Engineering Is Changing. Here’s What It Means for Data Scientists
The landscape of data science is evolving, and context engineering is at the forefront of this shift. This article explores the latest guidelines reshaping how data scientists work, moving beyond traditional approaches to unlock deeper insights. Discover practical applications of these advancements to streamline your workflows and elevate your data analysis. If you're curious about the evolving role of AI coding agents, consider “When to Use Claude Code and When to Use Codex” for further exploration of this related topic.
Enterprise AI agents are only as reliable as the messiest documents behind them
Enterprise AI's potential is often hampered by the disorganized data underpinning it. While context engineering—connecting systems, generating embeddings, and building retrieval pipelines—works for isolated assistants, it treats enterprise knowledge as application-specific, leading to inconsistency and duplicated effort. As AI deployments expand, managing enterprise knowledge itself becomes paramount. A shared enterprise knowledge platform, akin to an enterprise data platform, offers a solution, organizing knowledge into layers for preservation, normalization, integration, and optimized serving—a foundation for reliable, scalable AI.
TrueFoundry's open source AI agent harness TrueForge boasts 30%-75% cheaper task completion than Claude Managed Agents
TrueFoundry introduces TrueForge, a new open-source AI agent harness designed to empower enterprise developers and reduce costs. Built by former Meta and Google engineers, TrueForge offers a vendor-neutral solution, compatible with various AI models and deployable across different infrastructures. Initial testing reveals impressive cost savings—up to 75% less than Anthropic’s Claude Managed Agents—achieved through intelligent context engineering.
Presentation: The Right 300 Tokens Beat 100k Noisy Ones: The Architecture of Context Engineering
Coding agents often falter, not due to insufficient context, but due to excessive and noisy input. In "The Right 300 Tokens Beat 100k Noisy Ones: The Architecture of Context Engineering," Baruch Sadogursky and Patrick Debois reveal why bloated context windows hinder performance and present practical fixes. Learn about lazy-loaded skills, versioned artifacts, and externalized memory—techniques to transform raw markdown into reliable agentic workflows.
Graph Engineering for AI Agents: Beyond the Single-Agent Loop
AI agent development is evolving beyond autonomous loops, with graph engineering emerging as a critical next step. This approach reframes AI applications as explicitly designed workflows, orchestrating agents, tools, and data sources for optimal coordination. Graph engineering defines these interactions, offering a more structured and predictable path toward complex AI solutions. Explore how this paradigm shift moves beyond the single-agent perspective—a concept further detailed in "MCP Explained: How Modern AI Agents Connect to the Real World"—and unlocks new possibilities for intelligent automation.
AI Root Cause Analysis Shifts from Model Reasoning to Context Engineering
The emerging paradigm in AI root cause analysis is shifting. Rather than relying solely on model reasoning, engineers are increasingly focused on “context engineering”— preparing data pipelines that effectively correlate telemetry. Early findings from a Coroot experiment across eleven models offer compelling initial evidence supporting this claim. This represents a significant shift, suggesting the hard problem lies in data preparation, not inherent model limitations.
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Optimizing Retrieval-Augmented Generation (RAG) systems hinges on precise question parsing. Loop Engineering for RAG, detailed in our latest Enterprise Document Intelligence report [Vol.1 #6quinquies], introduces a streamlined approach: a deliberately small loop focused on question refinement. This involves reading the document, identifying gaps, and re-parsing the query—a critical step before retrieval. Explore this technique to enhance accuracy and efficiency. For a foundational understanding of iterative learning processes, consider “Backpropagation Explained for Beginners (Part 1).”
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
The conversation around loop engineering often centers on Large Language Models (LLMs), but can the architecture itself drive improved performance? This article presents a novel experiment, rigorously testing a deterministic, zero-dependency Python benchmark to isolate failures—without an LLM. Results across 300 random seeds demonstrate that goal-directed controllers consistently outperform linear pipelines in completing independent branches. Explore the architecture, benchmark, and debugging process, revealing that failure isolation is, in fact, a measurable property of control flow.
Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Unlock the full potential of Retrieval-Augmented Generation (RAG) with Context Engineering for Question Parsing. This approach transforms raw, unstructured questions into precisely typed fields, directly steering both retrieval and generation processes. Published in Enterprise Document Intelligence [Vol.1 #6quater], this post details a critical technique for maximizing AI agent effectiveness. Addressing the "AI context gap," as explored in our related article, "The AI context gap: Enterprise AI organizations have a trust problem…", this method ensures your AI agents operate with clarity and precision.