Sub-25ms Agent Reflexes: Why Tool Choice Doesn't Need a 70B Model

The Autoregressive Bottleneck in Coding Agents

When a human developer works inside an IDE, deciding to run git status or inspect package.json requires zero deliberation. It is a mechanical reflex. Yet in modern AI agent harnesses—such as Cursor, Claude Code, Cline, and Windsurf—every single intermediate tool choice currently traverses a massive 70-billion or 200-billion parameter autoregressive language model.

Consider the typical lifecycle of a 10-turn coding task:

1. Inspect working tree status (git status)
2. Read configuration file (view_file)
3. Run test runner (npm test)
4. Grep for failing symbol (grep_search)
5. Inspect stack trace
6. Edit single function
7. Re-run tests
8. Format code
9. Verify git diff
10. Commit changes

Out of these 10 turns, 8 are purely mechanical. They do not require novel creative prose or poetic synthesis. They require deterministic routing with closed argument schemas. Yet each turn costs between 1,200ms and 2,400ms of time-to-first-token and upwards of $0.015 in upstream inference bills.

TRADITIONAL AGENT TURN (Autoregressive Frontier LLM):
[Agent State] ---> [Encode 128k Context] ---> [Forward Pass 70B Weights] ---> [Sample Next Token 80x] ---> [1,420ms | $0.0150]

JEVPROXY SHORT-CIRCUIT (System One Calibrated Engine):
[Agent State] ---> [Non-Autoregressive Match] ---> [Direct Tool Decision] ---> [18.4ms | $0.0001]

System One vs. System Two: Kahneman's Principles for Agents

In cognitive psychology, Daniel Kahneman distinguished between System 1 (fast, instinctual, mechanical reactions) and System 2 (slow, deliberate, analytical reasoning). Today's frontier LLMs are universally operated as System 2 engines, even when asked to answer questions as basic as 'Should I run git status next?'

JevProxy acts as the missing System One reflex layer. By intercepting tool call arrays directly at the proxy layer, JevProxy matches agent state against calibrated decision models. If confidence is ≥95%, the tool execution is short-circuited in sub-25ms. If open-ended creative code synthesis is required, the prompt passes straight through upstream to Claude 3.5 Sonnet or GPT-4o with zero penalty.

Empirical Latency & Cost Reductions

In our test harness across 10,000 developer agent sessions, average task completion time dropped from 44.8 seconds to 8.2 seconds—an over 5.4x end-to-end acceleration. Concurrently, API token bills plummeted by 91.4% because deterministic intermediate turns bypass $15/M token pricing entirely.

Conclusion: The Separation of Reflex and Reasoning

Autonomous coding software cannot scale if mechanical tool selection costs two seconds per step. By decoupling fast deterministic decisions from slow creative reasoning, developer tools can achieve the snappy responsiveness of native compilers while retaining the frontier intelligence of modern foundation models.