A brand new framework from Stanford College and SambaNova addresses a important problem in constructing sturdy AI brokers: context engineering. Known as Agentic Context Engineering (ACE), the framework routinely populates and modifies the context window of huge language mannequin (LLM) functions by treating it as an “evolving playbook” that creates and refines methods because the agent beneficial properties expertise in its atmosphere.
ACE is designed to beat key limitations of different context-engineering frameworks, stopping the mannequin’s context from degrading because it accumulates extra info. Experiments present that ACE works for each optimizing system prompts and managing an agent's reminiscence, outperforming different strategies whereas additionally being considerably extra environment friendly.
The problem of context engineering
Superior AI functions that use LLMs largely depend on "context adaptation," or context engineering, to information their habits. As an alternative of the expensive means of retraining or fine-tuning the mannequin, builders use the LLM’s in-context studying talents to information its habits by modifying the enter prompts with particular directions, reasoning steps, or domain-specific data. This extra info is normally obtained because the agent interacts with its atmosphere and gathers new knowledge and expertise. The important thing objective of context engineering is to arrange this new info in a means that improves the mannequin’s efficiency and avoids complicated it. This method is turning into a central paradigm for constructing succesful, scalable, and self-improving AI methods.
Context engineering has a number of benefits for enterprise functions. Contexts are interpretable for each customers and builders, might be up to date with new data at runtime, and might be shared throughout completely different fashions. Context engineering additionally advantages from ongoing {hardware} and software program advances, such because the rising context home windows of LLMs and environment friendly inference strategies like immediate and context caching.
There are numerous automated context-engineering strategies, however most of them face two key limitations. The primary is a “brevity bias,” the place immediate optimization strategies are inclined to favor concise, generic directions over complete, detailed ones. This will undermine efficiency in advanced domains.
The second, extra extreme situation is "context collapse." When an LLM is tasked with repeatedly rewriting its complete collected context, it could possibly endure from a type of digital amnesia.
“What we call ‘context collapse’ happens when an AI tries to rewrite or compress everything it has learned into a single new version of its prompt or memory,” the researchers mentioned in written feedback to VentureBeat. “Over time, that rewriting process erases important details—like overwriting a document so many times that key notes disappear. In customer-facing systems, this could mean a support agent suddenly losing awareness of past interactions… causing erratic or inconsistent behavior.”
The researchers argue that “contexts should function not as concise summaries, but as comprehensive, evolving playbooks—detailed, inclusive, and rich with domain insights.” This method leans into the power of contemporary LLMs, which might successfully distill relevance from lengthy and detailed contexts.
How Agentic Context Engineering (ACE) works
ACE is a framework for complete context adaptation designed for each offline duties, like system immediate optimization, and on-line situations, comparable to real-time reminiscence updates for brokers. Reasonably than compressing info, ACE treats the context like a dynamic playbook that gathers and organizes methods over time.
The framework divides the labor throughout three specialised roles: a Generator, a Reflector, and a Curator. This modular design is impressed by “how humans learn—experimenting, reflecting, and consolidating—while avoiding the bottleneck of overloading a single model with all responsibilities,” in accordance with the paper.
The workflow begins with the Generator, which produces reasoning paths for enter prompts, highlighting each efficient methods and customary errors. The Reflector then analyzes these paths to extract key classes. Lastly, the Curator synthesizes these classes into compact updates and merges them into the present playbook.
To forestall context collapse and brevity bias, ACE incorporates two key design rules. First, it makes use of incremental updates. The context is represented as a group of structured, itemized bullets as an alternative of a single block of textual content. This permits ACE to make granular adjustments and retrieve probably the most related info with out rewriting your complete context.
Second, ACE makes use of a “grow-and-refine” mechanism. As new experiences are gathered, new bullets are appended to the playbook and present ones are up to date. A de-duplication step commonly removes redundant entries, making certain the context stays complete but related and compact over time.
ACE in motion
The researchers evaluated ACE on two varieties of duties that profit from evolving context: agent benchmarks requiring multi-turn reasoning and gear use, and domain-specific monetary evaluation benchmarks demanding specialised data. For top-stakes industries like finance, the advantages lengthen past pure efficiency. Because the researchers mentioned, the framework is “far more transparent: a compliance officer can literally read what the AI learned, since it’s stored in human-readable text rather than hidden in billions of parameters.”
The outcomes confirmed that ACE constantly outperformed sturdy baselines comparable to GEPA and basic in-context studying, reaching common efficiency beneficial properties of 10.6% on agent duties and eight.6% on domain-specific benchmarks in each offline and on-line settings.
Critically, ACE can construct efficient contexts by analyzing the suggestions from its actions and atmosphere as an alternative of requiring manually labeled knowledge. The researchers observe that this skill is a "key ingredient for self-improving LLMs and agents." On the general public AppWorld benchmark, designed to judge agentic methods, an agent utilizing ACE with a smaller open-source mannequin (DeepSeek-V3.1) matched the efficiency of the top-ranked, GPT-4.1-powered agent on common and surpassed it on the tougher check set.
The takeaway for companies is critical. “This means companies don’t have to depend on massive proprietary models to stay competitive,” the analysis group mentioned. “They can deploy local models, protect sensitive data, and still get top-tier results by continuously refining context instead of retraining weights.”
Past accuracy, ACE proved to be extremely environment friendly. It adapts to new duties with a median 86.9% decrease latency than present strategies and requires fewer steps and tokens. The researchers level out that this effectivity demonstrates that “scalable self-improvement can be achieved with both higher accuracy and lower overhead.”
For enterprises involved about inference prices, the researchers level out that the longer contexts produced by ACE don’t translate to proportionally greater prices. Trendy serving infrastructures are more and more optimized for long-context workloads with strategies like KV cache reuse, compression, and offloading, which amortize the price of dealing with in depth context.
In the end, ACE factors towards a future the place AI methods are dynamic and constantly enhancing. "Today, only AI engineers can update models, but context engineering opens the door for domain experts—lawyers, analysts, doctors—to directly shape what the AI knows by editing its contextual playbook," the researchers mentioned. This additionally makes governance extra sensible. "Selective unlearning turns into way more tractable: if a chunk of data is outdated or legally delicate, it could possibly merely be eliminated or changed within the context, with out retraining the mannequin.”
