Patent-Pending Framework

Your AI remembers everything.
That's the problem.

Every AI session accumulates dead weight — context your agent no longer needs but keeps paying for. AEL measures it, models it, and tells you exactly when to cut it loose.

Read the Research Get the Tool

The Problem

AI agents waste your money by default.

The inefficiency is structural — it's baked into how AI sessions work. Nobody measures it. We do.

📈

Costs compound every turn

Every token added to context gets paid for again on every subsequent API call. The overhead doesn't grow linearly — it accelerates like a wedge.

🔧

Tool calls multiply the drag

A single turn with 6 tool calls re-reads the full context 6 times. One heavy turn can cost more than $0.13 in pure overhead — for context your agent already processed.

📉

Stop paying for context your agent should forget

File reads, browser snapshots, completed task logs — all marked irrelevant after use, all still in context, all still billing. The efficiency limit is real.


The Proof

We found where AI spend goes to die.

Real numbers from a real productive session — UI development, code edits, analysis. Not a stress test. A normal day.

$5.10
Total session cost
$3.37
Context overhead (66%)
$1.73
Actual inference (work done)
2–4×
More turns for the same spend with AEL
⚠️ Simulation figures assume linear context growth and perfect classification. Real deployments typically see 2–4× improvement. Read the full methodology →

The Theory

The efficiency limit your AI provider won't tell you about.

AEL is a formal framework — not a heuristic. It derives the exact spawn condition from first principles and caching economics.

When an AI agent's context fills with irrelevant content, every subsequent turn pays to re-read it. The Agent Efficiency Limit defines the precise moment when spawning a fresh session — with a clean, minimal context — becomes cheaper than continuing.

The spawn condition is derived from the ratio of cached to uncached token pricing. Across every major AI provider, this ratio is 10:1. That's not a coincidence — it's a structural property AEL exploits.

The framework includes a Bayesian update rule for improving spawn timing over time, a formal proof of optimality, and empirical validation on real sessions.

7 Provisional Patents Filed — March 27, 2026
The Spawn Condition
Spawn when: V_shed > S_real

// V_shed: cost of irrelevant tokens per turn
V_shed = δ=0_tokens × k_cached

// S_real: true cost to spawn a fresh session
S_real = S_fixed + S_variable

// Breakeven ratio (all major providers)
k_cached / k_uncached = 0.10

// Carry if referenced >1 in 10 turns.
// Shed if less. File size cancels out.

The Tools

Smarter agents don't just do more — they carry less.

AEL tools plug into your existing agent infrastructure. No rewrites. No new framework. Just efficiency.

Coming Soon

AEL Sidecar

Zero-code integration for any agent framework. Drop it alongside your existing setup — LangChain, CrewAI, OpenAI Agents, or raw API. No agent changes required.

Coming Soon

AEL Extension

Browser extension for monitoring AI sessions directly in Claude.ai, ChatGPT, and other web interfaces. AEL for non-developers.


FAQ

Common questions

Everything you need to know about AEL, the Dashboard, pricing, and supported frameworks.

Read the full FAQ →

The gap between what AI costs and what it should cost.
We built the bridge.

Read the research, explore the tools, or get in touch to discuss licensing and integration.

Read the Research Get in Touch