229 stories
Teams of AI agents barely outperform solo agents but cost up to 5.1x more, according to Vals AI. Only one out of four tests with GPT-6 Sol and Claude Opus 5.5 showed a measurable gain. Anthropic's own data backs this up: beyond ten agents, quality plateaus while token costs keep climbing.
I won a $7,000 hackathon prize. The payout form offered me Wise. Wise's own help page says that personal accounts with an address in Indonesia "can no longer hold money or currencies in their account." That is the kind of mistake I built Cair to prevent: an agent that plans how a prize or client…
Spending cycles manually writing tests, or constantly updating brittle automation, can feel like a Sisyphean task. We know comprehensive testing is crucial for stable applications, but the sheer volume and diversity of cases often mean we cut corners or fall behind.
An AI agent runs with your credentials and acts before you can stop it. GitGuardian analyzed nine public incidents between May 2025 and July 2026 to reverse-engineer what breaks in production.
Part 2: the bridge connects the team. What can the team turn into something useful? In the first article, I connected my personal assistant, Instinct, to OpenCode on my Mac. Instinct is the PM. OpenCode is the developer.
Hello again, and welcome back to Fast Company’s Plugged In. In recent years, being gobsmacked by AI’s ever-expanding capabilities has become my new normal. More importantly, I’ve found the technology increasingly essential to my own productivity. What AI never did until last week was save my bacon.
I recently completed the AI-Assisted Data Science with BigQuery lab via Google Cloud. While analyzing the final multimodal vector search stage, I focused heavily on how the pipeline enforces state dependencies.
Most "agents" I ship are a while loop with an LLM call inside. That's fine. The hard part isn't the loop, it's the policy around it: which functions the model is allowed to trigger, which failures justify trying another model, and where the loop stops.
AI agent governance is the set of rules, checks, and controls that decide what an autonomous agent is allowed to do, see, and act on once it's live. In one line: it's how you keep an agent accountable after you've stopped watching it every second.
OpenClaw 2.0 is not a model release. It is the milestone name for OpenClaw v2026.8.1, which shipped on August 30, 2026, and it converts a mostly single-player agent runtime into a shared, persistent operating layer.
Your AGENTS.md adds 15,000 tokens to every API call. Trim it, cache it, and route heartbeats to save $139/month. Here is the 10-minute fix.
[Codelab] Author LookML with Agentic Coding Tools → https://g.dev/ai/looker-agentic-lookml Learn about the 5 core tools in the Agentic Looker Developer Stack: Looker Skills, Looker CLI, Looker Managed MCP server, MCP-toolbox-for-databases, and the Looker VSCode Extension.
Paste one prompt and your Grok Bot installs LUCI Core, then answers what it saw, read, or did earlier from its own screen history. Setup, examples, limits.
The Engram plugin gives Claude Code persistent, long-term memory, so it remembers what you've done and brings it back when it matters.
A survey conducted by independent research firm Coleman Parkes on behalf of Undo, a company focused on scaling AI-powered root-cause analysis, found that while AI coding agents have accelerated code generation, they have shifted the primary bottleneck to debugging, code comprehension, and…
Originally published on Codeloop Software. Quick answer: MCP (Model Context Protocol) is an open standard created by Anthropic that defines how AI applications connect to external tools and data sources — like USB for AI, you build one MCP server and any MCP-compatible client can use it.
Originally published on Codeloop Software. Quick answer: In 2026, a custom single-purpose AI agent (lead qualification, support deflection) costs $1,500–$5,000 to build plus $300–$800/month to run.
If you spend any time around coding agents in 2026, you've absorbed the advice: use multiple agents. One plans, one implements, one reviews. The implication runs one direction. Two agents beat one, and if two are good, three should be better.
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