Desk
Autonomous and semi-autonomous software agents.
SpaceXAI's Grok Voice 3.0 tops an independent speech-to-speech index at an unchanged price — strong overall, but not the leader on agentic task completion.
Siemens EDA lifted its chip-characterization agent from about 55% to 95% on a verified benchmark by grading final artifacts, not individual steps — a lesson for every engineering agent.
Meta’s Muse Wonder puts a frontier agent model on a public Model API at $4/$20 per million tokens, with Wonder Full gated behind paid Muse Code plans.
Shopify is letting shoppers point any AI agent at its billion-plus listings via Shop—human approval still required, platform power shifting toward agent checkout.
Z.ai's GLM-Legal nearly triples its base model on a tough legal agent benchmark at a sliver of frontier cost, but it's gated, slow and not actually the top scorer.
Meta’s Superintelligence Labs is shipping Muse Wonder and Wonder Full into paid Code subscribers, claiming parity with GPT-6 Astra and Claude’s top models—before the agent itself runs on them.
Sakana AI stopped grading AI reviewers on how human they sound and started grading them on whether they catch planted mistakes. That is the right test, and the results are encouraging and humbling at once.
OpenAI and Meta are now selling their agents on privacy, each one cast as the safer choice. A promise is not an architecture, and the gap between the two is where people get burned.
Microsoft is betting the next useful AI layer does not talk at all. Decision-1 picks from fixed options fast and cheap, but every number behind it comes from Microsoft's own scoreboard.
Asana says an AI coding agent found why its browser agent was burning money, then fixed it. The 76x headline is narrow, but the method behind it is the useful part.
Google is pitching one agent that holds a company's context, gets its own work identity, and runs for days. The governance design is serious; the proof is still mostly anecdote.
NVIDIA's second-place KDD Cup team made a small model reliable by shrinking what the agent could do, and that lesson matters more than any leaderboard spot.