The Context Layer Your AI Is Missing

Context engineering that makes LLMs accurate, reliable, and efficient.

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LLMs are highly sensitive to gaps, decay, and misplacement in their contextual data

LLMs are sensitive to gaps, decay, and misplacement.

LLMs are highly sensitive to gaps, decay, and misplacement in their contextual data

Why Agentic Systems fail today

Poor Retrieval

The system processes initial context effectively. Early information is captured and retained with high accuracy. Critical insight: Retrieval performance degrades significantly when key facts appear mid-sequence, causing a 15-25% drop in accuracy. This middle-position blindness affects model reliability. Final conclusions are processed well, but the crucial middle content often gets overlooked.

mid-blind • 100% accuracy

Poor Retrieval & Prioritization

LLMs often miss the right facts at the right time.

Performance dropped 15–25% when key information is mid-sequence rather than at the start or end (Liu et al., 2024)

Context Rot

300 tokens • 89% accuracy

Context Rot

Model performance drops across long context windows.

Accuracy falls from 89% → 51% when expanding from 300 → 113k tokens (Chroma, 2025)

No Structured Context

chaos level • 0%

No Structured Context Management

Missing context governance leads to unreliable outputs.

41.77% of multi-agent breakdowns stemmed from context and organizational errors (Cemri et al., 2023)

Distilling the context down to the smallest amount of relevant tokens possible

Distilling context into high-signal, minimal tokens.

Distilling the context down to the smallest amount of relevant tokens possible

Context Engineering is the missing puzzle piece

Kayba converts noisy knowledge into compact, high-signal context bundles so LLMs make better, faster, and more reliable decisions.

Token Compaction

Fewer, higher-value tokens reduce context overhead and inference spend.

Token Compaction

Fewer, higher-value tokens reduce context overhead and inference spend.

Token Compaction

Fewer, higher-value tokens reduce context overhead and inference spend.

Context Orchestration

Preserve critical facts so models make correct calls consistently.

Context Orchestration

Preserve critical facts so models make correct calls consistently.

Context Orchestration

Preserve critical facts so models make correct calls consistently.

Retrieval & Prioritization

Hybrid retrievers + rerankers to surface the right facts first.

Retrieval & Prioritization

Hybrid retrievers + rerankers to surface the right facts first.

Retrieval & Prioritization

Hybrid retrievers + rerankers to surface the right facts first.

We don't just talk about context engineering solutions—We build them

One memory layer across all AI tools

We don't just talk about context engineering solutions—We build them

We built the Agentic Context Engine (ACE): allowing agents to learn by experience

Instead of making the same mistakes over and over, agents powered by the Agentic Context Engine (ACE) learn from execution feedback — understanding what works, what doesn’t, and improving with every run.

ACE is an open-source framework. Just plug it in and watch your agents get smarter — no training data, no fine-tuning, just continuous, automatic improvement.

Benefits: better performance, self-improving behavior, no context collapse, and compatibility with 100+ LLMs.

Based on the Agentic Context Engineering paper and inspired by Dynamic Cheatsheet.

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Playbook

(Evolving Context)

• Strategy Bullets
✓ Helpful strategies
✗ Harmful patterns
○ Neutral observations

Generator

Executes task using playbook

Task Environment

Provides feedback + optional ground truth

Reflector

Analyzes and provides feedback what was helpful/harmful

Curator

Produces improvement deltas

Merger

Updates the playbook with deltas

We don't just talk about context engineering solutions—We build them

One memory layer across all AI tools

We don't just talk about context engineering solutions—We build them

We already built TeamLayer: a shared memory hub for AI

We designed and shipped TeamLayer: proof that context engineering works. A persistent memory layer syncs context across your AI tools so you don’t copy-paste or start over.

A 30-min session to unpack your current constraints and use cases.

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This session focuses on your pains and processes—not a pitch. We’ll trace your workflow, surface friction points, and outline immediate wins. Select a time and add notes or links.

This session focuses on your pains and processes—not a pitch. We’ll trace your workflow, surface friction points, and outline immediate wins. Select a time and add notes or links.