Feature hasn't been suggested before.
Describe the enhancement you want to request
Proposal: Agentic Context Engineering (ACE) Implementation - Technical Review Needed
Context
I am proposing the implementation of the paper Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models (ICLR 2026), by Zhang et al. (Stanford/SambaNova).
What has been done
I used an AI agent (running on a fork of OpenCode) to attempt implementing ACE, and have now released the skills that made that process possible: ACE-implementation
The repository contains:
- 9 specialized skills designed for an AI agent to build ACE components (Foundation, Agentic Roles, Semantic Intelligence, Core Integration, Multi-Epoch Reflection, etc.)
- Integration pattern with Model2Vec for embeddings (chosen for extreme performance)
- Structured documentation that guided the agent through the implementation
Important: These are NOT a working implementation - they are prompt-based skills that allow an AI agent to implement ACE in any codebase. Think of them as a "recipe" that attempts to reproduce the process I went through.
Why a fresh implementation by developers is the right approach
I lack the technical competence to evaluate whether these skills will actually produce efficient code. I used Gemini 3.1 Pro and MiniMax to assist in developing these skills, and the resulting prompts are in a much better standard than I could write alone. However, without adequate technical knowledge, I cannot distinguish what works well from what might be wrong.
This is exactly why I am sharing these skills - not as a final product, but as a starting point. A proper implementation built from scratch by developers who understand the codebase would be far superior to anything an AI agent could produce following my prompts.
Why this matters: ACE Advantages
The paper presents significant advances:
-
Brevity Bias Resolution: Previous methods lost domain-specific insights when summarizing to concise instructions. ACE preserves details through structured, incremental updates.
-
Context Collapse Prevention: Previous iterative rewrites degraded information over time. ACE uses modular updates that maintain detailed knowledge.
-
Self-learning without labeled supervision: The framework can adapt using natural execution feedback, not requiring expensive labels.
-
Impressive results:
- +10.6% on agent benchmarks (AppWorld)
- +8.6% on finance (FiNER, Formula)
- 86.9% lower adaptation latency
- Matched the top-ranked agent using only a smaller open-source model
What this means for a Terminal Agent: Cognitive Advancement
For a terminal agent, ACE represents a significant cognitive leap:
- Continuous Evolution: Context is not static - it accumulates, refines, and organizes strategies automatically through multiple iterations
- Adaptive Memory: Each interaction improves the agent's playbook, allowing it to learn from successes and failures
- Efficiency: Low computational overhead with high adaptation quality, reducing API costs and latency
- Scalability: Works both offline (system prompts) and online (agent memory), adapting to different scenarios
- Self-Improvement: Ability to improve without human intervention, using natural execution feedback
What I'm asking for
I suggest developers evaluate the skills and consider:
- Test if the skills can guide an agent to produce a working ACE implementation
- Use them as inspiration to build a proper implementation from scratch (which would be much better)
- Identify inefficiencies or gaps in the prompts
- Contribute improvements or create a better version
The skills may not be perfect, but I believe they can serve as a reference for how an AI agent approached the problem. The theoretical foundation of the paper is solid and the results justify serious exploration.
If we're having a problem with too many code suggestions from AI, it seems more plausible to implement it with my AI code, transform the entire process into a SKILL, and ask someone more competent to do this implementation in a supervised manner.
I sincerely hope this content is helpful, because using Gemini 3.1 cost me $10.
Repository link: https://github.com/charmandercha/ACE-implementation
Paper: https://arxiv.org/abs/2510.04618
Feature hasn't been suggested before.
Describe the enhancement you want to request
Proposal: Agentic Context Engineering (ACE) Implementation - Technical Review Needed
Context
I am proposing the implementation of the paper Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models (ICLR 2026), by Zhang et al. (Stanford/SambaNova).
What has been done
I used an AI agent (running on a fork of OpenCode) to attempt implementing ACE, and have now released the skills that made that process possible: ACE-implementation
The repository contains:
Important: These are NOT a working implementation - they are prompt-based skills that allow an AI agent to implement ACE in any codebase. Think of them as a "recipe" that attempts to reproduce the process I went through.
Why a fresh implementation by developers is the right approach
I lack the technical competence to evaluate whether these skills will actually produce efficient code. I used Gemini 3.1 Pro and MiniMax to assist in developing these skills, and the resulting prompts are in a much better standard than I could write alone. However, without adequate technical knowledge, I cannot distinguish what works well from what might be wrong.
This is exactly why I am sharing these skills - not as a final product, but as a starting point. A proper implementation built from scratch by developers who understand the codebase would be far superior to anything an AI agent could produce following my prompts.
Why this matters: ACE Advantages
The paper presents significant advances:
Brevity Bias Resolution: Previous methods lost domain-specific insights when summarizing to concise instructions. ACE preserves details through structured, incremental updates.
Context Collapse Prevention: Previous iterative rewrites degraded information over time. ACE uses modular updates that maintain detailed knowledge.
Self-learning without labeled supervision: The framework can adapt using natural execution feedback, not requiring expensive labels.
Impressive results:
What this means for a Terminal Agent: Cognitive Advancement
For a terminal agent, ACE represents a significant cognitive leap:
What I'm asking for
I suggest developers evaluate the skills and consider:
The skills may not be perfect, but I believe they can serve as a reference for how an AI agent approached the problem. The theoretical foundation of the paper is solid and the results justify serious exploration.
If we're having a problem with too many code suggestions from AI, it seems more plausible to implement it with my AI code, transform the entire process into a SKILL, and ask someone more competent to do this implementation in a supervised manner.
I sincerely hope this content is helpful, because using Gemini 3.1 cost me $10.
Repository link: https://github.com/charmandercha/ACE-implementation
Paper: https://arxiv.org/abs/2510.04618