ai-agents.
72 writings found
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Docker Sandboxes Give AI Agents Real Isolation, Not Just Containers
Docker's new Sandbox Kits and Cloud Sandboxes create reproducible, isolated environments for AI agents. Here's why that matters for the future of autonomous workflows.
Cloudflare's cf CLI: When Tools Are Built for Agents, Not Humans
Cloudflare's new cf CLI expands from 280 to 3000+ operations and prioritizes agent usability over human workflows. What does this mean for the future of developer tools?
Holo4 Shows Open Models Can Handle Real Business Workflows
H company's new agentic models combine GUI, API, and code interfaces in one system, challenging the notion that only frontier models can automate complex business tasks.
Holo4: The Multi-Interface Agent That Actually Works
New agentic models that seamlessly switch between GUIs, APIs, and code. Why this matters for real business automation.
Docker Sandboxes Give AI Agents Real Boundaries
Docker's new Sandbox Kits and Cloud Sandboxes create reproducible, isolated environments for AI agents. Here's why that matters for the future of autonomous tooling.
Why Your AI Agent Works Once But Fails the Next Time
Most benchmarks hide consistency problems behind averages. A new diagnostic tool exposes why capable agents produce unreliable results and how to fix it.
Why Your AI Agent Works Once But Fails the Second Time
Consistency gaps in LLM agents are hiding in plain sight. Why average accuracy masks unreliability, and how to measure what users actually care about.
Why Your AI Agent Succeeds 77% of the Time (But Only 53% Reliably)
Most agent benchmarks hide inconsistency behind averages. Here's why that gap matters and how to measure real-world reliability.
Rewriting 800K Lines with AI Agents: What We Learned
GitHub rewrote the Copilot agent runtime from TypeScript to Rust using AI agents. Here's what that massive migration reveals about the future of large-scale rewrites.
Why Your AI Agent Works Once But Fails the Next Time
Consistency gaps in LLM agents matter more than average accuracy. Introducing consistency guidelines to stabilize agent decisions.
Why Your AI Agent Works Once but Fails Twice
AI agents achieve high average accuracy but fail inconsistently on identical tasks. A new consistency measurement and guideline system reveals why, and how to fix it.
ToolGrad Flips the Script on AI Agent Training
Google researchers reveal how generating tool-use solutions first, then prompts, creates better training data for LLMs with 100% pass rates and lower costs.
ToolGrad Flips AI Agent Training on Its Head
Google researchers show that generating tool-use solutions before prompts leads to cheaper, more reliable AI agent training datasets and better model performance.
ToolGrad Flips the Script on AI Agent Training
Google researchers reverse dataset generation: build tool-use chains first, then queries. The result? Cheaper, more reliable AI agent training with smaller models outperforming proprietary ones.
ToolGrad Flips the Script on AI Agent Training
Google researchers reveal how generating tool-use answers before queries cuts costs and improves LLM performance. What this means for the future of AI agents.