JIT Context OS – Epistemic context runtime for coding agents

JIT-Context OS, an open-source epistemic context runtime for coding agents developed by Wojciech Wiesner of The Ones, reports a 2.44x faster task delivery time (3m 52s vs. 9m 27s) and a 61.4% reduction in LLM inference turns (66 vs. 171) compared to standard long-context methods in a benchmark on the Synthapse Web Audio codebase. The runtime, version 0.2.4, claims to eliminate error loops and reduce prompt context size by over 99% (482 tokens vs. >50,000 tokens) through its Epistemic Invariants and SQLite-based hot-path storage.

Version: 0.2.4 2026-09-08 Author: Wojciech Wiesner wojciech@theones.io — The Ones join.theones.io DOI: 10.5281/zenodo.22649542 https://doi.org/10.5281/zenodo.22649542 Repository: https://github.com/wojciechwiesner/jit-context https://github.com/wojciechwiesner/jit-context Paired Production Codebase Benchmark Synthapse Web Audio , 45 modules, Vitest E2E : Head-to-head evaluation of autonomous coding agents under standard long-context dump vs. JIT-Context OS: | Metric | Baseline Standard Long-Context | JIT-Context OS | Production Moat / Delta | |---|---|---|---| | Task Delivery Time | 9m 27s 567s | 3m 52s 232s | 2.44x Faster Delivery -59.0% | | LLM Inference Turns | 171 API calls | 66 API calls | -61.4% Turns -105 rounds avoided | | Tool Execution Churn | 169 tool ops | 64 tool ops | -62.1% Agent Churn Reduction | | File Read Churn | 73 file reads | 24 file reads | -67.1% Less Context Thrashing | | Scope Drift / Collateral Edits | 14 files touched drift | 4 files surgical SRP | Zero Scope Drift | | Runtime & Test Error Loops | 8 error loops | 0 errors clean first-shot | 100% Error Loop Elimination | | Prompt Context Size | 50,000 tokens Haystack | 482 tokens Capsule | 99% Token Reduction | | Hot-Path RYOW Latency | 200–800ms Vector API | <3ms SQLite WAL | Zero-Latency Ground Truth | | Epistemic Invariants | Vulnerable to self-poisoning | 10/10 PASS I1–I10 | 100% Anti-Hallucination Gate | Modern LLM-based autonomous agent architectures suffer from the "Haystack Tax" : context window inflation 50k–100k+ tokens , severe attention degradation Lost-in-the-Middle , self-poisoning through recursive consumption of prior assistant speculation, and severe rate-limiting 429 Too Many Requests / rolling context window exhaustion . JIT-Context is a universal, deterministic, multi-tier temporal memory and context runtime with Epistemic Invariants I1–I10 . It serves as an architectural drop-in runtime for autonomous agents Claude Code, Hermes, Cursor, OpenCode, Codex, and custom multi-agent harnesses . By decoupling hot-path operational state <3ms local SQLite WAL from slow associative brokers and enforcing strict authority weighting User Authority = 1.0, Assistant Speculation = 0.0, Runtime Tool Proof = 1.0 , JIT-Context compiles a Lean Context Capsule 400–1,200 tokens, ceiling <1,500 tokens just-in-time for each turn. | Capability / Challenge | Semantic Vector Stores Mem0 / Zep | Self-Managed Agent Memory Letta / MemGPT | Context Stuffing 100k+ Dump / Prompt Caching | JIT-Context This Architecture | |---|---|---|---|---| | Hot-Path Latency | Slow 200–800ms API / embedding | Moderate LLM decides tool call | Zero Static Prompt | <3ms Local SQLite WAL, In-Memory | | Read-Your-Own-Writes | Eventual consistency / indexing lag | Delayed by multi-turn tool loops | N/A Frozen context | Instant RYOW <0.5ms | | Self-Poisoning Vulnerability | HIGH re-ingests assistant answers | HIGH agent writes own core facts | MODERATE hallucinations stay in transcript | ZERO Assistant Epistemic Weight = 0.0 | | Scope Drift Resistance | LOW fuzzy similarity pulls other repos | LOW unconstrained agent queries | VERY LOW lost-in-the-middle confusion | HIGH L1 Scope Hysteresis Guard | | Prompt Cache Alignment | POOR dynamic injected text breaks cache | POOR frequent core memory edits | MODERATE large prefix, expensive cache misses | 95% Cache Hit Rate Prefix-stable | | Hot-Reloading in RAM | Requires service restart | Process rebuild | Re-prompting required | Dynamic In-Process Hot-Reload <1ms | | Token Economy | Inflates prompt with Top-K fragments | Multiple turns of tool-calling overhead | Severe $$$ context tax, rolling 5h limits | Lean Capsule 400–1,200 tokens | ┌───────────────────────────────────────────────┐ │ USER / TASK INPUT │ └───────────────────────┬───────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────────────────────┐ │ JIT-CONTEXT RUNTIME COMPILER │ │ │ │ ┌───────────────────────┐ ┌───────────────────────┐ ┌───────────────────────┐│ │ │ L0 HOT-PATH <3ms │ │ L1 WARM-PATH <10ms │ │ L2 DEEP-PATH 600ms ││ │ │ • SQLite WAL Overlay │ │ • Project Scope Cache │ │ • Circuit Breaker ││ │ │ • Read-Your-Own-Write│ │ • Scope Hysteresis │ │ • Fail-Open Policy ││ │ │ • Runtime Tool Proofs│ │ • Active vs Retrieval │ │ • Associative Broker ││ │ │ • Dynamic Hot-Reload │ │ • Session CWD Track │ │ • Ollama Fallback ││ │ └───────────┬───────────┘ └───────────┬───────────┘ └───────────┬───────────┘│ │ │ │ │ │ │ └───────────────────┬──────┴──────────────────────────┘ │ │ ▼ │ │ ┌─────────────────────────────┐ │ │ │ EPISTEMIC ARBITER I1–I10 │ │ │ │ • User Authority = 1.0 │ │ │ │ • Assistant Weight = 0.0 │ │ │ │ • Tool Proof Weight = 1.0 │ │ │ │ • Anti-Self-Poisoning Gate │ │ │ └──────────────┬──────────────┘ │ │ ▼ │ │ ┌─────────────────────────────┐ │ │ │ LEAN CONTEXT CAPSULE │ │ │ │ 400–1,200 TOKENS <10ms │ │ │ │ Ceiling <1,500 tokens │ │ │ └──────────────┬──────────────┘ │ └───────────────────────────────────┼──────────────────────────────────────────────┘ ▼ ┌───────────────────────────────┐ │ STABLE PREFIX LLM CACHE │ │ Gemini / Claude / GLM │ │ • 4.51x Faster TTFT │ │ • Zero 429 Rate Limits │ └───────────────────────────────┘ A live multi-module software engineering duel: autonomous agents were tasked with fixing 4 distinct root-cause bugs across 3 interconnected modules event pipeline.py , retry policy.py , storage.py verified by an independent pytest suite. | Competitor | Runtime & Configuration | Total Turns | Wall-Clock Time | Pytest Verification | API / Hardware Cost | Privacy Guarantee | |---|---|---|---|---|---|---| | 🥇 Local Qwen 3.8 9B + JIT | Local Metal M2 Pro Ollama 32k context | 4 turns | ~2 min 126s | 4/4 PASSED exit 0 | 0.00 PLN | 100% Local / Zero Data Leak | | 🥈 Gemini 3.8 Flash + JIT | Google Cloud Frontier API | 9 turns | 17.77s | 4/4 PASSED exit 0 | Paid Cloud API | Cloud API payload | | 🥉 Gemini 3.8 Flash WITHOUT JIT | Google Cloud Frontier API Raw Chat History | 10 turns | 34.57s | ❌ 0/4 FAILED | Paid Cloud API | Cloud API payload | 1. Local Model Turn Dominance: With JIT Context OS maintaining a calibrated ~1.8k token working set, the local 9B model on a Mac Mini resolved the multi-module task in only 4 turns — more than 2x fewer turns than Google's Gemini 3.8 Flash in the cloud. 2. Parallel Tool Calling Precision: In Turn 1, Qwen dispatched 4 parallel read file calls. In Turn 2, it executed 3 surgical parallel write file calls fixing all 4 root causes in one shot, passing tests on the first verification attempt. 3. The Haystack Failure Mode: Without JIT, Google's flagship Gemini 3.8 Flash got lost in conversational history and tests/ directory loops, failing to resolve the issue within the turn budget. A head-to-head paired benchmark was executed on the production repository Synthapse Web Audio Generative AI Techno Instrument, ~45 modules, Vitest + Vite build . Autonomous agents were tasked with implementing direct MP3 audio export and recording alongside WAV: | Metric | With JIT-Context Calibrated Capsule | Control No JIT / Long-Context | Delta / Real Impact | |---|---|---|---| | Wall Clock Time | 232.43 s 3m 52s | 567.57 s 9m 27s | -59.0% 2.44x faster | | LLM API Calls Turns | 66 | 171 | -61.4% 105 rounds avoided | | Total Tool Calls | 64 | 169 | -62.1% | | Files Read read file | 24 | 73 | -67.1% 3x less context churn | | Discovery Ops Before Edit | 31 | 65 | -52.3% | | Time to First Code Mutation | 125 s | 202 s | -38.1% -77s | | Runtime / Test / Patch Errors | 0 100% clean | 8 errors tests, syntax, patch | 100% error loop elimination | | Scope Drift Files Touched | 4 files surgical SRP | 14 files severe drift into DJ/Studio | Zero scope drift | | Context Capsule Size | 482 tokens calibrated architecture | Monolithic workspace dump 50k tok | 100x leaner context window | | Verification Suite | PASS 199/199 Vitest tests | PASS 216/216 tests after 8 fixes | First-shot clean build | Evaluates Invariant I3 & I4 against gemini-3.8-flash : when an assistant falsely asserts a service is running and deployed, but the physical tool returned exit code: 1 Port 8080 already bound : - Control No JIT / Raw Chat History : The LLM read previous assistant claims, compounding the speculation. - JIT Context OS v0.2.4: Assistant text rejected weight 0.0 . Physical tool output compiled into VERIFIED RUNTIME PROOFS Authority 1.0 . - Result: 100% Grounded Accuracy — model answered: "NIE – w tej sesji nie przeprowadzono wdrożenia ani nie wykonano weryfikacji uruchomieniowej potwierdzającej działanie serwisu na porcie 8080." - EXP-006 Synthapse DSP Arbitration : -86.3% prompt tokens 774 vs 5,659 , 1.4x faster latency 3.24s vs 4.55s , 100% exact retrieval of in-flight audio parameters 142 BPM, 3200 Hz cutoff, sidechain OFF . - EXP-007 Tuli.my Relational Epistemics : -76.6% prompt tokens 783 vs 3,347 , 100% strict adherence to user-defined boundary overrides. - I1 Direct User Input Supremacy : Unambiguous human instructions instantly supersede all prior assumptions with Authority = 1.0. - I2 Atomic Monotonic Sequence : Every turn and state change receives an atomic sequence number RETURNING seq in SQLite WAL. - I3 Anti-Self-Poisoning : Assistant generated text is assigned epistemic weight = 0.0 . Speculations never pollute canonical truth. - I4 Read-Your-Own-Writes / RYOW : Updates committed in turn$N$ are guaranteed readable in turn$N+1$ in$<0.5$ ms. - I5 Scope Hysteresis : Cross-project queries expand retrieval scope without thrashing the primary active workspace. - I6 Double Circuit Breaker & Fail-Open : L2 broker latency is capped at 600ms. Triple network failures trigger an open circuit with zero downtime. - I7 Bounded Derived Authority : Inferred observations from tools are capped at Authority = 0.70 until physically verified exit code: 0 = 1.0 . - I8 Event Idempotency & Budget Ceiling : Dynamic capsules strictly bounded below <1,500 tokens to preserve prompt caching stability. - I9 Deterministic Fallback : If memory subsystems fail, the agent falls back to pristine system prompts seamlessly. - I10 Prompt Caching Prefix Alignment : Frozen system manifests are positioned before dynamic capsules, achieving 95% prompt cache hit rates. jit-context/ ├── benchmarks/ Empirical and synthetic benchmark datasets │ ├── EXP-001-CANARY.json │ ├── EXP-002-STATISTICAL-20.json │ ├── EXP-003-TRI-VARIANT.json │ ├── EXP-004-ABLATION.json │ └── EXP-005-REAL-WORLD-SYNTHAPSE.json ├── src/ │ ├── context/ │ │ ├── compiler.py Multi-tier capsule compiler <10ms │ │ └── cascade distiller.py 3-tier cascade distiller & elastic budget │ ├── health/ │ │ ├── doctor.py Automated system verification CLI │ │ ├── exp005 runner.py Needle-in-haystack benchmark │ │ ├── exp006 synthapse.py DSP state arbitration benchmark │ │ ├── exp007 tulimy.py Relational boundary benchmark │ │ └── exp008 tool epistemics.py Runtime tool anti-hallucination test │ ├── hooks.py Hermes & agent harness integration hooks │ ├── init.py /jit init project profiler CLI │ ├── l0/ │ │ ├── db.py SQLite WAL database layer <1ms │ │ ├── epistemics.py Authority & epistemic weighting engine │ │ └── overlay.py Read-Your-Own-Writes operational overlay │ ├── l1/ │ │ ├── project cache.py Scope cache with hysteresis <10ms │ │ └── scope.py Scope resolution with hysteresis │ ├── l2/ │ │ ├── broker client.py Deep retrieval client with circuit breaker │ │ └── distill.py Context compressor & pruner │ ├── telemetry/ │ │ └── observatory.py Realtime HTTP telemetry server :8765 │ └── tests/ 27 automated unit and regression tests ├── CITATION.cff CERN Zenodo citation metadata ├── MANIFEST.sha256 Cryptographic file integrity manifest └── README.md Clone the repository git clone https://github.com/wojciechwiesner/jit-context.git cd jit-context Install package & dependencies pip install -e . Run the 27 automated unit & invariant tests pytest src/tests/ Run Doctor check verifying 10/10 Invariants python3 src/health/doctor.py Start local metrics server on port 8765 python3 src/telemetry/observatory.py open http://127.0.0.1:8765/ If you use or reference JIT-Context in academic research or production agent frameworks, please cite: @software{wiesner2026jitcontext, author = {Wiesner, Wojciech}, title = {JIT-Context: An Epistemic Context Runtime for AI Agents}, year = 2026, publisher = {Zenodo}, version = {v0.2.4}, doi = {10.5281/zenodo.22649542}, url = {https://doi.org/10.5281/zenodo.22649542} } MIT License — Copyright c 2026 Wojciech Wiesner wojciech@theones.io — The Ones join.theones.io .