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In this episode, weāre diving into the biggest problem plaguing long-running LLM deployments: context drift and brevity bias. Your models start strong, but they decay over time, demanding costly retraining and frustrating MLOps teams. Static prompt engineering is a dead end.
Today, we're unlocking Agentic Context Engineering, or ACEāthe future of production AI. Detailed in an essential article by the experts at Diztel, ACE is not just a better prompt; itās a fully operational system layer. It allows your LLMs to learn through adaptive memory and evolve instructions automatically, just like a human team member.
Key Takeaways:
1. Instead of static prompts, Agentic Context Engineering (ACE) enables LLMs to ālearnā through instructions, examples, and adaptive memory.
2. ACE directly tackles context drift and brevity bias, the two biggest killers of long-running AI performance.
3. Think of ACE as turning prompt engineering into a system layer ā a pipeline that evolves and curates instructions automatically.
4. The real power of the framework? Evaluation and self-reflection are its core primitives, making it possible to benchmark and auto-improve agents over time, reducing the need for retraining.
5. ACE can integrate into enterprise MLOps pipelines as a governance layer, giving teams visibility into how context evolves, and offering levers for optimization without costly model retraining.
Tools / Tech : ACE (Agentic Context Engineering) is a conceptual framework with its own modular components:
āACE doesnāt just prompt models ā it teaches them how to remember, reflect, and evolve without retraining.ā
The results are insane:
ā 10.6% better than GPT-4 agents on AppWorld
ā 8.6% on finance reasoning
ā 86.9% lower cost and latency
If this scales, the next generation of AI will be self-tuned. We are entering the era of living prompts.
Link to research - https://www.arxiv.org/pdf/2510.04618
Credit: The Diztel Team - https://diztel.com
šStop Marketing to the General Public. Talk to Enterprise AI Builders.
Your platform solves the hardest challenge in tech: getting secure, compliant AI into production at scale.
But are you reaching the right 1%?
AI Unraveled is the single destination for senior enterprise leadersāCTOs, VPs of Engineering, and MLOps headsāwho need production-ready solutions like yours. They tune in for deep, uncompromised technical insight.
We have reserved a limited number of mid-roll ad spots for companies focused on high-stakes, governed AI infrastructure. This is not spray-and-pray advertising; it is a direct line to your most valuable buyers.
Don't wait for your competition to claim the remaining airtime. Secure your high-impact package immediately.
Secure Your Mid-Roll Spot: https://buy.stripe.com/4gMaEWcEpggWdr49kC0sU09
By Etienne Noumen4.6
1111 ratings
In this episode, weāre diving into the biggest problem plaguing long-running LLM deployments: context drift and brevity bias. Your models start strong, but they decay over time, demanding costly retraining and frustrating MLOps teams. Static prompt engineering is a dead end.
Today, we're unlocking Agentic Context Engineering, or ACEāthe future of production AI. Detailed in an essential article by the experts at Diztel, ACE is not just a better prompt; itās a fully operational system layer. It allows your LLMs to learn through adaptive memory and evolve instructions automatically, just like a human team member.
Key Takeaways:
1. Instead of static prompts, Agentic Context Engineering (ACE) enables LLMs to ālearnā through instructions, examples, and adaptive memory.
2. ACE directly tackles context drift and brevity bias, the two biggest killers of long-running AI performance.
3. Think of ACE as turning prompt engineering into a system layer ā a pipeline that evolves and curates instructions automatically.
4. The real power of the framework? Evaluation and self-reflection are its core primitives, making it possible to benchmark and auto-improve agents over time, reducing the need for retraining.
5. ACE can integrate into enterprise MLOps pipelines as a governance layer, giving teams visibility into how context evolves, and offering levers for optimization without costly model retraining.
Tools / Tech : ACE (Agentic Context Engineering) is a conceptual framework with its own modular components:
āACE doesnāt just prompt models ā it teaches them how to remember, reflect, and evolve without retraining.ā
The results are insane:
ā 10.6% better than GPT-4 agents on AppWorld
ā 8.6% on finance reasoning
ā 86.9% lower cost and latency
If this scales, the next generation of AI will be self-tuned. We are entering the era of living prompts.
Link to research - https://www.arxiv.org/pdf/2510.04618
Credit: The Diztel Team - https://diztel.com
šStop Marketing to the General Public. Talk to Enterprise AI Builders.
Your platform solves the hardest challenge in tech: getting secure, compliant AI into production at scale.
But are you reaching the right 1%?
AI Unraveled is the single destination for senior enterprise leadersāCTOs, VPs of Engineering, and MLOps headsāwho need production-ready solutions like yours. They tune in for deep, uncompromised technical insight.
We have reserved a limited number of mid-roll ad spots for companies focused on high-stakes, governed AI infrastructure. This is not spray-and-pray advertising; it is a direct line to your most valuable buyers.
Don't wait for your competition to claim the remaining airtime. Secure your high-impact package immediately.
Secure Your Mid-Roll Spot: https://buy.stripe.com/4gMaEWcEpggWdr49kC0sU09

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