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We've created a comprehensive Retrieval Harness for modern agentic retrieval in 2026. The harness provides a persistent data pipeline that can connect to a data source, index and update a large knowledge base, and expose a broad set of tools akin to filesystem operations (semantic/keyword search, regex grep, file... show more

Jerry Liu

84,698 subscribers

57,194 просмотров • 3 месяцев назад •via X (Twitter)

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Комментарии: 24

Фото профиля Adnan Qidwai

Adnan Qidwai3 месяцев назад

btw the GitHub doesn't seem public yet

Фото профиля Jerry Liu

Jerry Liu3 месяцев назад

sorry about the above - just made it public!

Фото профиля Ira Rothken

Ira Rothken3 месяцев назад

This looks very promising for AI legal apps and ESI analysis.

Фото профиля Kevin Madura

Kevin Madura3 месяцев назад

Have you tested it on OBLIQ-Bench?

Фото профиля Pinkman

Pinkman3 месяцев назад

hybrid search across the whole index with read, grep, and find is basically giving an agent the same tools a developer would use on a local filesystem

Фото профиля Eugene Yaroslavtsev

Eugene Yaroslavtsev3 месяцев назад

@jerryjliu0 i haven't been able to find a bug bounty / security program link on gh/llama's official site - got a link?

Фото профиля AW

AW3 месяцев назад

jerry,dropped you a DM about a paid collab

Фото профиля KYD 玩 ai

KYD 玩 ai3 месяцев назад

Treating retrieval as a persistent tool surface, with search, grep, file search, and read operations, feels much closer to how agents actually investigate large knowledge bases than one-shot RAG calls.

Фото профиля Dmytro Palaniichuk 🇺🇦

Dmytro Palaniichuk 🇺🇦3 месяцев назад

The filesystem metaphor is the right abstraction. Moving from "query the index" to "navigate the knowledge base" made agents far more predictable — they scope reads before writing, like a good engineer. Partial-update primitives, or is re-index always full corpus?

Фото профиля Christian Müller

Christian Müller3 месяцев назад

Persistent pipeline plus a bounded, filesystem-like tool surface is a strong combo here. The reliability jump comes when each tool call returns a typed result the next step can rely on, not free-form text the agent has to re-parse every run.

Фото профиля srijanarya

srijanarya3 месяцев назад

The filesystem-style tools were never my bottleneck. Index freshness was. "Index and update a large KB" buries the ugly part: an agent retrieving mid-reindex and answering off a stale chunk. Does the harness guarantee read-during-update consistency?

Фото профиля Sean Kerr — AI, minus the hype

Sean Kerr — AI, minus the hype3 месяцев назад

The persistent pipeline is the right call. Most "agentic retrieval" pain isn't recall — it's the agent re-indexing the world every run because nothing survives between calls. The harder half: teaching it when it has enough to stop retrieving.

Фото профиля Nahid

Nahid3 месяцев назад

persistent data pipeline opens up new possibilities

Фото профиля Shashank Nidhi

Shashank Nidhi3 месяцев назад

Your harness builds a fresh index, but it never flags when a source document drifts. Staleness is a harder problem than retrieval. A vector store can answer a query, yet it has no idea the underlying truth moved two weeks ago. If you attach a provenance check on each update, the agent can decide whether to trust the result.

Фото профиля Nhost

Nhost3 месяцев назад

The quiet shift here is that retrieval starts looking more like an agent exploring a filesystem: search, grep, read, follow the thread. That's much closer to how people actually research a case

Фото профиля Oleksandr

Oleksandr3 месяцев назад

agentic retrieval feels like giving an agent a local filesystem watching legal agents actually crawl PDFs is next‑level

Фото профиля Anshuman Singh

Anshuman Singh3 месяцев назад

The persistent-pipeline framing matches what we hit with travel data: static snapshots rot fast, so continuous dedup plus per-row source tags are what keep agent citations honest. Curious how the harness handles provenance when two indexed sources conflict, do you keep both with lineage or resolve to one?

Фото профиля Jabir | AI Systems

Jabir | AI Systems3 месяцев назад

👍🏻

Фото профиля Sebastian Buzdugan

Sebastian Buzdugan3 месяцев назад

index v2 will live or die on permission drift, not tool breadth

Фото профиля Pawzard

Pawzard3 месяцев назад

agents out here getting a whole filing cabinet meanwhile i still cant find my keys 😭

Фото профиля The Guy With A Hat

The Guy With A Hat3 месяцев назад

Another OCR. Sweet, is there a paper attached?

Фото профиля egesea

egesea3 месяцев назад

Retrieval is becoming less about finding information. It's becoming the workspace where agents think and act.

Фото профиля Makaroni

Makaroni3 месяцев назад

curious how it handles drift tho like the knowledge base updates mid-task and the agent already cached stale context

Фото профиля Brosko

Brosko3 месяцев назад

ngl 'filesystem operations but for vibes' is actually the unlock agents been fumbling retrieval tasks for too long fr 🔥

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183,637 просмотров • 1 год назад

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Avi Patel

91,697 просмотров • 1 год назад

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This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

420,133 просмотров • 5 месяцев назад

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

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Clara Shih

49,651 просмотров • 2 лет назад

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Aaron Levie

74,286 просмотров • 3 месяцев назад