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Showing 101–120 of 338 dossiers
Google's agent-accessible data toolkit has moved beyond its August launch: GA expands support to Bigtable, BigQuery Graph and Spark, with IDE/CLI integration, IAM enforcement and no separate kit fee. Underlying Google Cloud usage still costs money.
The May Antigravity agent ID is retired. Managed Agents now require the September preview ID and default to Gemini 3.8 Flash, alongside hooks, token budgets and scheduled sandboxes.
From December 3, agent workflows that ask Atlassian's Teamwork Graph for cross-product context will need a cost budget. Most enriched tool calls use 1–10 Rovo credits, with paid overages at $0.01 per credit.
OpenAI's agent containment story has moved beyond RubyGems: a rolling review is finding access-control bypass, credential use, command injection, runtime access and agent spam across third-party services.
DeepSeek V4.1 Flash supersedes the old Flash and Vision-Exp API lines with native multimodality, lower pricing and new architecture. Unlike those retired Flash aliases, the current DeepSeek API changelog and rate card still show V4 Pro as a distinct service.
The live DeepSeek changelog and rate card still show distinct V4 Pro service after the previously announced September 14 reroute. That changes cost and model-selection assumptions.
GitHub has moved local Copilot sandboxes from preview to GA. Enterprises can now combine centrally managed approval policies with operating-system-enforced limits on what coding agents can actually reach.
Together Link connects six existing coding-agent/desktop harnesses to open models with reversible profiles, per-session routing and cost receipts. The important shift is portability at the harness boundary, not Together's unverified savings claim.
The useful boundary change is that Copilot can now cross from code and terminals into ordinary desktop interfaces, with per-app approval and organisation-level controls.
Pi’s first stable release is interesting less for another coding-agent version number than for what its deliberately minimal core now considers mature enough to include: MCP, code-driven tool orchestration and model routing.
Canvas moves AI store building into production theme code, but the official requirements make the maintenance boundary clearer: entering Canvas can cut off normal theme downloads and upstream theme updates.
The bug is a useful warning for AI application plumbing: turning a user-supplied URL into a model attachment also turns the application server into a network client unless the adapter enforces an outbound trust boundary.
Jev made bounded decision models visible; Strands Decider makes the pattern reproducible inside an agent stack. AWS replaced Qwen3.5-2B's language-generation head with a small scoring head and released the recipe, creating a local alternative for decisions that do not need a full generative model.
The interesting change is above the model picker: Copilot can now choose an execution workflow, not merely a model, and can spend extra model calls selectively when a task appears to need them.
AMD is not just buying another AI software company. It is buying a frontier model lab so the workloads behind spatial intelligence, robotics and simulation can help shape the compute stack AMD builds next.
The Agent Host’s environment boundary has moved from local Dev Containers to remote development hosts, making persistent coding-agent sessions more portable across real remote projects.
This is a hard capability removal rather than a routine model migration. Products built on OpenAI’s video-generation API now need another provider or a redesigned video path because the official deprecation table offers no successor endpoint.
Investigations has crossed from preview into production and incident.io now reports a large latency improvement in its own measured workflow. The agent continuously reassesses evidence and can hand remediation to coding agents, but the new speed and accuracy figures remain vendor-produced rather than independent.
GLiNER2.5-Decide attacks the same bounded-decision layer as Jev and CLM from a much smaller encoder architecture. Its strongest benchmark claims are vendor-produced, but CPU deployment and constrained joint decoding make it a materially different option for software-facing AI decisions.
CLM-8B targets the same narrow decision layer as Jev, but with open weights, local deployment and a contrastive architecture that separates state and action representations. The headline speed and coding results are researcher-produced and need careful interpretation.