## Overview Contextual is a company focused on context engineering for production LLM systems, founded and led by Nina Latutina who brings a background in neuroscience and reward learning. The company has positioned itself at the forefront of what they term "context engineering," which encompasses the full spectrum of techniques needed to effectively manage and optimize context windows in large language model applications. The discussion, which appears to take place around the NeurIPS conference timeframe, covers both the company's specific platform capabilities and broader trends in the context engineering space. The core problem Contextual addresses is the challenge of building production-ready retrieval-augmented generation systems and agentic workflows that can effectively reason over large document collections while maintaining precision and avoiding well-known failure modes like context rot. Their platform aims to provide an end-to-end solution that works across multiple domains including e-commerce, legal, code generation, and device testing, with a focus on enabling rapid prototyping followed by production optimization. ## Context Engineering as a Discipline Context engineering has emerged as a critical discipline over the past year, representing the transition from simple RAG implementations to more sophisticated approaches that manage context windows strategically. The discussion highlights several key failure modes that context engineering addresses, particularly context rot, which refers to the degradation in retrieval performance as context windows grow larger. Research cited suggests that at around 700,000 tokens in a million-token context window, retrieval accuracy can drop to approximately 30%. This phenomenon has become widely recognized in the field, with influential work from researchers at Chroma and others establishing it as a baseline concern for production systems. The state of context engineering is characterized as being in a prototyping stage, with many design patterns emerging but no uniform architecture yet established. Development typically follows a pattern of initially allowing agents to use unlimited tokens and then progressively constraining and optimizing through techniques like KV caching and other efficiency improvements. The expectation is that the next phase will involve scaling these approaches to handle increasingly complex tasks. ## The Evolution from RAG to Agentic RAG A significant theme in the discussion is the evolution from traditional RAG to what's termed "agentic RAG." While the debate over terminology may be somewhat overrated, the practical improvements are substantial. Even incremental steps like query reformulation, where an initial query is broken down into subqueries that can better match relevant documents, show dramatic performance improvements. This approach has become a new baseline for production systems. The query reformulation process involves taking a user query and decomposing it into multiple parallel searches that can be executed simultaneously and then recombined. This parallelization is a key performance optimization. Work done on systems like Sweet Graph demonstrated training models to handle six parallel searches at baseline, scaling up to eight, which significantly exceeds the typical one to four parallel operations seen in standard tool-calling implementations. Additionally, there's an emphasis on limited agency where agents are incentivized through reinforcement learning to terminate and return answers rather than running indefinitely. ## The Contextual Platform Architecture Contextual's platform is built around several key components working together as an integrated system. The architecture begins with multimodal ingestion that can handle various document types including PDFs, log files, and large CSV tables. A critical capability is extracting and maintaining the hierarchical structure of document contents, which enables more intelligent retrieval later in the pipeline. The retrieval pipeline combines multiple techniques including filters, hybrid search that blends keyword and semantic approaches, and reranking. Contextual released the first instruction-following reranker in March and has continued to update it. The instruction-following capability is particularly important for dynamic agent applications, though it comes with latency tradeoffs that are less critical in agentic scenarios where the agent can take whatever time is needed to complete its reasoning process. Reranking serves a specific purpose in the context engineering workflow: when reasoning over large databases, the system needs high recall in initial retrievals to ensure relevant information isn't missed, but requires high precision for what actually gets placed into the context window to avoid context rot and maintain performance. The reranker bridges this gap by narrowing down the initial retrieval results to the most relevant subset. ## Dynamic Agents and Sub-Agents A major focus of Contextual's approach is the use of dynamic agents composed of constrained sub-agents. This architecture has emerged as a best practice for production systems, moving away from overly general agents toward specialized components that excel at specific tasks. Sub-agents can be fine-tuned for their particular responsibilities, enabling effective hill climbing and optimization. The company's experience in a context engineering hackathon held in San Francisco in mid-November provides concrete evidence for the value of this approach. The hackathon, hosted by Brian Bishoff and Hamill Hussein, featured a dataset called Retail Universe with just under 100,000 documents including PDFs, log files, and large CSVs in the retail domain. Participants had to answer challenging queries and generate structured output. Contextual's dynamic agent achieved approximately 25 points compared to the winner's 29 points and a human benchmark of 23 points, demonstrating superhuman performance. The leading team used a combination of Mixtral and Claude, while the second-place team used Cursor. Through this experience, Contextual identified several critical constraints for production systems. With datasets of that scale, sub-agents would naturally want to take excessive turns to exhaustively search the data, and they would also repeatedly check their work over and over. Implementing explicit turn limits and validation constraints on sub-agents proved essential for maintaining reasonable performance and latency. ## MCP Integration and Tool Selection Model Context Protocol integration represents both an opportunity and a challenge for context engineering. MCP provides a standardized way to connect tools and data sources, but it introduces its own context management problems. MCP manifests as large JSON structures at the front of the prompt containing descriptions of all available tools, which can quickly lead to context rot when even ten tools are included, especially if those tools have verbose descriptions. Contextual has developed an innovative solution to this problem by using their reranker for MCP server selection. Rather than including all available MCP servers in the context, the system can intelligently select which servers are most relevant for a given task. This meta-level application of context engineering techniques helps manage the proliferation of tools. The company's experience with MCP follows a common pattern: it's excellent for rapid prototyping and demonstrating value in early versions, allowing developers to quickly combine tools and build demos. However, for production systems, there's a migration path toward direct API calls that reduce complexity and dependencies. This pragmatic approach recognizes MCP's value in different stages of the development lifecycle while acknowledging its limitations at scale. The Anthropic MCP registry appears to be designed primarily for agent consumption rather than human browsing, which raises interesting questions about tool selection. While there's clear value in enabling agents to autonomously discover and use tools, human oversight remains important for security and reliability considerations. The industry is still working out the right balance between human-in-the-loop and fully autonomous tool selection. ## Prompt Optimization and Automated Context Engineering Contextual has explored automated prompt optimization techniques including approaches like JAPA, which represents an evolution of the DSPy concept. JAPA implements a form of continual prompt learning where LLMs optimize their own prompts by examining outputs and reasoning about improvements, similar to a PyTorch training loop but operating only on prompts rather than model weights. It incorporates genetic evolutionary elements where multiple prompt samples are generated and the best-performing ones are selected. The discussion also highlights work on ACE (Agentic Context Engineering), which has shown superior benchmark performance on financial and complex document sets. A key insight from ACE is that approaches which completely rewrite prompts from scratch or go through many compress-expand-compress cycles can lose critical information and see performance degradation. Instead, ACE uses an agentic approach to make smaller, incremental tweaks to the current prompt rather than wholesale rewrites. This more conservative approach helps maintain context stability and prevents the confusion and hallucinations that can occur when agents operate in rapidly changing environments. ## KV Cache Management and Multi-Turn Conversations KV caching represents both an efficiency optimization and a potential performance improvement for context engineering. The basic principle is to separate static content that doesn't change (typically placed at the front of the prompt) from dynamic content that varies with each turn. For multi-turn agent conversations, the cache naturally contains the previous turns while the system prompt remains relatively stable. The primary use case where KV caching provides significant cost savings is when serving the same prompt to many customers, though this is less common in highly personalized applications. More importantly, KV caching can improve performance by providing a more stable environment for agents across multiple turns. However, multi-turn conversations present ongoing challenges that existing systems don't handle particularly well. Even in advanced tools like Cursor, users often find themselves opening new windows mid-conversation because context bloat degrades performance. This has led to a practice of intentional context compression, where systems or users proactively compress context at regular intervals rather than trusting the model to maintain coherent understanding of very long conversations. Major model providers including Anthropic, OpenAI, and Google are implementing internal compaction mechanisms within their frontier models, though the effectiveness of these approaches continues to evolve. Real-world testing suggests that even sophisticated applications struggle once conversations extend beyond certain lengths, highlighting this as an ongoing area for improvement in production LLM systems. ## Cross-Domain Applicability Contextual's stated goal is to create an end-to-end platform that works across any domain and use case with minimal customization. The hackathon experience in e-commerce retail demonstrated this capability, as the team successfully applied their platform to a domain they hadn't extensively worked with previously. Similar success has been achieved in code generation, specifically for test code generation for devices, where Contextual achieved the highest human-based evaluation scores compared to existing coding platforms. The consistent approach across domains involves the same core pipeline: multimodal ingestion with hierarchy preservation, hybrid search with filters and reranking, and dynamic agent architectures. While some customization is required for each domain, the fundamental context engineering principles remain constant. This suggests that context engineering techniques are indeed becoming more generalizable and that platforms can provide value across diverse applications rather than requiring completely different architectures for each domain. ## Benchmarking and Evaluation Benchmarking represents an ongoing challenge in the context engineering space. Most research benchmarks use relatively small datasets that don't reflect production-scale requirements. The Retail Universe dataset from the hackathon, with its nearly 100,000 documents totaling potentially billions of tokens, represents the kind of scale that production systems actually need to handle but that most benchmarks don't test. There's a notable trend of benchmarks being saturated shortly after release. The HOW benchmark from Princeton, which includes tasks like recreating research papers, was saturated by Claude Code earlier this week after being released only in October. The evaluation required human assessment because the AI solutions sometimes took different but equally valid approaches compared to the gold standard, and in some cases actually corrected errors in the reference data. This rapid benchmark saturation is expected to continue as capabilities improve, particularly for increasingly challenging and complex tasks. The industry would benefit from more benchmarks designed around production-scale scenarios rather than toy examples, as these would better reflect the actual challenges of deploying context engineering systems at scale. ## Production Patterns and System Design The current state of context engineering is characterized by component-level innovations, with discussions focusing on individual elements like memory systems, rerankers, or specific design patterns for context compression. The predicted evolution is toward full-system design patterns where the discussion shifts from optimizing individual components to architecting complete systems with well-understood tradeoffs and configurations. This evolution mirrors the maturation of other engineering disciplines, moving from an experimental phase where practitioners try various approaches to a more established phase with recognized patterns and architectures. The sub-agent architecture represents an early example of such a pattern, where constrained, specialized agents handle specific tasks and can be fine-tuned independently, providing clear paths for optimization and improvement. Turn limits and validation constraints on agents have emerged as important guardrails for production systems. Without these constraints, agents can waste resources on exhaustive searching or excessive validation loops. The challenge is finding the right balance between giving agents enough freedom to thoroughly explore solution spaces while preventing runaway resource consumption and maintaining reasonable latency for end users. ## Industry Context and Trends The broader context for these developments includes ongoing debates about model scaling and the role of small versus large models. Data from OpenRouter's state of AI survey shows that small models (under 15 billion parameters) are actually trending down in market share over time, while medium models (15 to 70 billion parameters) are trending up and large models remain stable. This contradicts some predictions about the rise of small, on-device models, which have struggled to deliver compelling experiences as evidenced by the perceived challenges with Apple Intelligence. For general-purpose models, larger continues to be better in practice, though for specialized component models like rerankers, smaller models may be preferable due to latency constraints. This distinction between general-purpose and specialized use cases is important for understanding where different model sizes make sense in production architectures. The discussion of scaling laws and the future of AI, including perspectives from researchers like Yejin Choi at Nvidia who focuses on small language models, suggests that smaller models might enable more compute usage overall by running on more devices, though this hasn't yet materialized in practice at scale. The reality is that for context engineering applications, the trend has been toward using larger, more capable models where they're available, with optimization focusing more on how context is managed than on model size reduction. ## Looking Forward The field of context engineering is expected to progress from its current prototyping stage to production scale over the coming year, with the types of tasks that can be reliably solved continuing to expand. The rapid saturation of increasingly difficult benchmarks suggests that capabilities are advancing quickly, and the emergence of system-level design patterns will help teams deploy these capabilities more reliably. Key areas for continued development include better handling of very long multi-turn conversations, more sophisticated tool selection and management strategies, improved prompt optimization techniques that maintain stability while enabling improvement, and industry-standard benchmarks that reflect production-scale requirements. The integration of all these components into cohesive systems that can be deployed across domains with minimal customization represents the next frontier for context engineering as a discipline.