Official product page from **DeepSeek**, published on **August 13, 2026**, **unsigned**, ~450 words, announcing the *developer preview* release of **DeepSeek Harness** (`dsh`) — a coding-agent harness **open source under the MIT license**, whose repository opened the same day. A three-word thesis, repeated in the title and in the repository description: *« Everything is a plugin »*, paired with a second promise, *« Every run is traceable »*. The page states the equation *« AGENT = MODEL + HARNESS »* and lists the pluggable capabilities — *« models, tools, skills, sessions, sandboxes, storage, loops, scheduling, and the UI »*. Four modes ship: **Standard** (full coding agent), **Code** (tools exposed via the *Code Mode SDK*, letting the model compose multi-step operations inside a TypeScript program), **Minimal** (*« two-tool coding agent with persistent bash and str_replace_editor »*, explicitly *« for benchmarking models in a minimal environment »*), and **Creator** (runtime inspection, in-memory plugin testing). The technical substance sits in the repository, not on the page: `docs/architecture.md` states a logging invariant — *« Model-visible means logged. Anything that reaches a model request must be reconstructable from the log, and a runtime invariant asserts it »* — and states that *« there is no privileged core to patch »*. The technical core is not DeepSeek's own: DSH is built on **Cordis** (the `cordiverse` project, a third party), **vendored** into `vendor/` with a manifest and a sync procedure, and the page places the *« Cordis paper »* at the same navigation level as "GitHub" and "Developer docs". Two LLM adapters ship — `dsh-llm-deepseek` and `dsh-llm-pi-ai`, a generic multi-provider adapter. The repository warns in capitals: *« THERE WILL BE COMPATIBILITY-BREAKING CHANGES »*, and `CLAUDE.md` specifies that `SESSION_FORMAT_VERSION` stays at `0` *« with no compatibility promise »*, with backends rejecting old on-disk formats. Timeline: DSH ships on the day **DeepSeek-V4-Pro reaches GA**, three days before a new API pricing schedule takes effect on **August 16, 2026 at 16:00 UTC**, with peak/off-peak rates and an off-peak discount of **−50%**.
#DeepSeek Harness#dsh#agent harness
**DeepSeek** (DeepSeek AI, laboratoire chinois) · en tant qu'institution. Page produit **non signée** : aucun auteur · aucun ingénieur mis en avant · aucun billet de blog ni papier technique associé. Le « nous » n'apparaît qu'une fois · en dernière phrase — *« We look forward to exploring the limits of intelligence with developers worldwide »*. Publiée le **13 août 2026**. La page est rendue en JavaScript : `curl` sur l'URL renvoie **HTTP 202 avec un corps vide** · le texte n'existant qu'après exécution du bundle. Deux documents de politique sont liés en pied de page — *Safe Use Policy* et *Data Processing Statement*.
Experience report published on **LinkedIn Pulse** on **August 12, 2026** by **Guillaume Dumortier**, in his newsletter *Growth Marketing Fit*, subtitled *« Four layers, a lot of rebuilding, and the failure modes nobody warns you about »*, ~2,500 words. The subject: an internal AI system built **in Claude** for a marketing team of about sixty people — roughly thirty content and sales **skills**, a dozen **source-of-truth modules**, **seven agents, six of which exist only to check work rather than produce it**, a **plugin** for those who live in a terminal, a **browser application** carrying the same knowledge for everyone else, and an orchestration that chains three or four assets into a *campaign bundle*. The thesis is set out early: the quality of an AI output is not determined at the moment of generation, but by what the system knows before it starts and by what happens to the draft afterward — *« The generation step in the middle is the easy part. It's also the only part most teams have built. »* Hence four layers: **Truth** (almost nobody builds it), **Production** (everybody), **Verification** (almost nobody), **Internal distribution** (*« where good systems die of neglect »*). Two failure mechanisms carry the article. **(A) The verifier's bare closed-world « pass »**: a fact-checker backed by product documentation receives a draft containing a claim about another product, one its sources did not cover — it returns a *« pass »*, not because the claim was true but because nothing contradicted it. *« It didn't just miss the error, it certified it. »* Fix: forbid a bare verdict and require every report to declare its **own coverage** — how many claims were checked, how many matched to sources, which fell outside its jurisdiction, which were owned by no source. *« "I can't verify this" became a first-class result. »* **(B) The cross-asset contradiction**: two assets can each be individually correct, each traceable to a real source, and still contradict each other — the press release states one date, the blog post another, both pass, the bundle can't ship. *« Per-asset verification can't catch that, by construction. »* Article's closing clause: *« The generation is free. The trust is the product. »*
**Guillaume Dumortier** — auteur de la newsletter LinkedIn **Growth Marketing Fit** (~1 300 abonnés à la publication). Il écrit en **praticien-constructeur** : il a passé *« une longue partie de cette année »* à bâtir et exploiter le système décrit. La légende de l'illustration précise le socle technique — *« A custom-built Marketing AI OS within Claude »*. Publié le **12 août 2026**.
Announcement from **Google** on **August 6, 2026**: Google joins as **Core Maintainer** the **Agent Plugins 1.0.0** specification, an open, *vendor-neutral* packaging format for distributing **Agent Skills** and **MCP servers** together. The specification was published by a **TSC** whose Core Maintainers come from **Amazon, Cursor, Microsoft, OpenAI, and Vercel**; Google joins them, represented by **Kevin Hou** (Senior Staff Engineer, Google DeepMind). The two packaged building blocks — Agent Skills and MCP — originate from **Anthropic**, which does not appear on this list of maintainers. **The diagnosis** fits in one sentence: *"The core problem isn't the components. It's the manifest."* A skill is portable, an MCP server is portable; the box they go in is not, and every client had to invent it for itself — hence the forks, the copies of identical components, and their drift. **The format** fits in one constraint: *"A plugin is a directory. That's the whole idea, and the restraint is the point."* A `plugin.json` with two useful lines (`$schema` and `name`), skills in `skills/` in the Agent Skills format, servers declared in `mcp.json` with an **explicit `type` on each entry** (stdio, Streamable HTTP, or the legacy HTTP+SSE) — no more transport guessed from the shape of the config object. The strength of the design lies in what the manifest **cannot** do: neither relocate components nor declare them inline, so there is no discovery path to configure and no precedence order to learn. Operational corollary: components **fail independently** — an `mcp.json` server that fails to start does not take down the plugin's skills, the client skips the entry, keeps going, and reports the failure. The accepted escape hatch is the **reverse-domain** directory (`com.example.client/`), an extension space owned entirely by one client (hooks, agents, commands) that other clients ignore: *"the portable core stays small because the non-portable parts have somewhere legitimate to go."* A section is dedicated to cases where the format is not warranted — *"Not every skill should be a Plugin"*: a single MCP server to a single client, `mcp.json` suffices; a single skill needs no plugin. What v1 explicitly excludes, under *future considerations*: **no installation mechanism, no distribution protocol, no permissions model, no sandboxing requirement, no trust or provenance verification, no UX**. All of this fits into an independently adoptable four-layer stack — **find** (Agentic Resource Discovery), **describe** (AI Catalog, which would register the `application/agent-plugins+json` type), **package** (Agent Plugins), **run** (MCP + Agent Skills). Two Google products already ship: **Agents CLI** and **Data Agent Kit** (BigQuery, Spanner, Cloud SQL).