On July 9, Meta Superintelligence Labs (MSL) released Muse Spark 1.1, the first major version upgrade of Muse Spark since April, with positioning formally shifting from "multimodal reasoning model" to "agentic foundation": the same weights now pack Main + Subagent orchestration, zero-shot MCP, adaptive Computer Use, visual programming, and the OpenAI-compatible Meta Model API public beta. The most noteworthy things aren't any single metric, but the fact that three things are now wound together: First, 1M token context + active compaction. 1.1 can actively retrieve earlier operation traces, compress noise by importance, and only keep the intermediate state that's truly needed for subsequent steps. This directly hits the long-horizon Agent's most painful place — "amnesia" in the middle of a conversation causes refactor to fail. Meta is pulling context management from engineering tricks to a model-internal capability. Second, true multi-Agent orchestration. The main agent collects context → makes a plan → dispatches parallel subagents; subagents know the tool boundary and escalate back to the main thread when stuck. This "role assignment + upward reporting" protocol was previously written by each vendor's own harness; now Meta has written it directly into the model policy. Third, Computer Use extends from "GUI automation" to "script vs click" either/or. The model decides at each step: if writing a script is faster, it just execs; if clicking the UI is simpler, it clicks; each step also batches out a set of actions. This pushes the same kind of workflow that Codex Computer Use and Claude Computer Use landed from "pointing and clicking" to "model picks its own tool". On the landing side, Meta simultaneously opened the Meta Model API (OpenAI-compatible public beta), with first partners Replit, Cline, and Box already integrated for end-to-end runs of long-context + tool-calling. Combined with the July 7 Muse Image, Spark 1.1 has actually stitched "perception (see images / see video) → reasoning (multi-Agent) → execution (Computer Use + Coding)" into a complete closed loop. On the industry side, this is the fourth piece of the July 9 frontier-stack puzzle (same day as Grok 4.5, GPT-5.6, and Ollama's $88M): the big labs are turning "harness-ready model + compatible API" into the new distribution unit, and pure chat models are starting to give way to "agentic runtime". One reservation: Meta's self-reported Meta Internal Coding Bench significantly outperforms the original and is "competitive with leading alternatives", but third-party independent evaluations of tool-use Agents are often the biggest gap between real-world and sandbox — worth running on your own repo before making production decisions.