Show HN: AgentJIT – Compile dynamic LLM agent workflows into 0.1ms Python
⚡ AgentJIT Just-In-Time Compiler for AI Agent Trajectories Compile flaky, 30-second multi-step AI Agent workflows into 5-millisecond deterministic code. Quickstart • Why AgentJIT? • Architecture • Benchmarks • Speculative Execution 💥 The Problem in 2026: Why AgentJIT? In 2026, autonomous AI agents solve real-world workflows across business, DevOps, and data analysis.
- ▪⚡ AgentJIT Just-In-Time Compiler for AI Agent Trajectories Compile flaky, 30-second multi-step AI Agent workflows into 5-millisecond deterministic code.
- ▪Quickstart • Why AgentJIT? • Architecture • Benchmarks • Speculative Execution 💥 The Problem in 2026: Why AgentJIT?
- ▪In 2026, autonomous AI agents solve real-world workflows across business, DevOps, and data analysis.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,652 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/eminsk/agentjit |
| Publication time | Sun, 13 Sep 2026 12:39:35 +0000 |
| Retrieval time | 2026-09-13T12:51:51.670Z |
| Last seen | 2026-09-13T12:51:51.670Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | SxphlvrHVBWE · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
⚡ AgentJIT Just-In-Time Compiler for AI Agent Trajectories Compile flaky, 30-second multi-step AI Agent workflows into 5-millisecond deterministic code. Quickstart • Why AgentJIT? • Architecture • Benchmarks • Speculative Execution 💥 The Problem in 2026: Why AgentJIT? In 2026, autonomous AI agents solve real-world workflows across business, DevOps, and data analysis. However, running stochastic LLM loops in production faces four critical barriers: Massive Latency: A standard 4-step agent workflow (think -> tool -> observe -> think) takes 15 to 45 seconds. Exponential Costs: Running the loop 10,000 times/day costs thousands of dollars in redundant API tokens. Flakiness & Hallucinations: Even 98% reliability per step leads to compounding errors across multi-turn trajectories.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.