I Built a JIT Compiler for AI Agents: How We Turned 30s LLM Chains into 0.1ms Deterministic Python

Description and use

AgentJIT compiles AI agent trajectories into fast, deterministic Python AST pipelines, reducing latency to <0.1 ms and eliminating token costs.

Problem

AI agents in production suffer from high latency, costly API usage, non‑determinism, and redundant inference when executing multi‑step tool chains.

Outcome

A 356× speedup (37.21 ms → 0.1044 ms), 100 % deterministic execution, zero token cost, and automatic speculative de‑optimization for safety.

Workflow

• 1. Decorate tools with @trace_tool and the agent with @jit.
• 2. First run triggers Tracer to record tool calls, inputs, outputs, and timings, building a DAG.
• 3. DAG Flow Analyzer resolves dependencies and arithmetic expressions.
• 4. AST Code Generator synthesizes pure Python AST with runtime guards.
• 5. Compiled JIT Pipeline runs subsequent invocations in <0.1 ms; guard failures deopt back to the LLM agent.

Evidence

We ran a 100‑iteration benchmark in Google Colab simulating an uncompiled multi‑step LLM chain versus the compiled AgentJIT pipeline: Mean Latency 37.21 ms vs 0.1044 ms (356.4× faster).

Source

Over 90 % of recurring invocations execute the exact same sequence of tool calls, differing only by input parameters.

Source