ai-agents
Open-Source Agent Frameworks in 2026: Hermes vs OpenClaw
Choosing an open-source agent framework is a bet on stability versus velocity. Here's a builder's decision guide, using this month's Hermes, OpenClaw, and Claude Code releases as live signals.
07/12/2026 · Industry Trends · 7 min read
Better Models, Worse Tools: Why AI Agents Still Feel Dumb in 2026
Frontier models keep getting smarter, yet the agents built on them still feel brittle. Here's why the bottleneck moved from model quality to tooling — and what that means for anyone shipping agents.
07/06/2026 · Industry Trends · 7 min read
Are AI Agents Overhyped? What Zuckerberg's "Slower Than Hoped" Admission Really Means
Mark Zuckerberg told Meta staff that AI agents haven't progressed as fast as he'd hoped. Here's what's real, what's stuck, and where agents already deliver value in 2026.
07/03/2026 · Industry Trends · 6 min read
OpenClaw Mobile: How to Set It Up on Android and iOS (v2026.6.11 Guide)
OpenClaw is finally on Android and iOS. Here's how to set it up via the OpenClaw Gateway, which messaging channels it supports, and what the v2026.6.11 reliability release fixes.
07/02/2026 · AI Tutorials · 6 min read
Prompt Injection Defense: A Builder's Guide to Securing AI Agents
Prompt injection defense is now a shipping requirement for anyone connecting an LLM to tools. Here is what the attack really is, why it can't be patched away, and the defense-in-depth layers to ship before your agent touches anything that matters.
06/30/2026 · AI Tutorials · 8 min read
AI Agents at Work: A Playbook for Deploying Them Without the Hype
AI agents at work are moving from demo to daily driver — Samsung is rolling ChatGPT and Codex to employees, and Notion retired its own email app because users prefer agents. Here's a strategic playbook for where agents pay off and how to deploy them.
06/29/2026 · Industry Trends · 8 min read
How to Evaluate AI Agents: A Practical Reliability Playbook
AI agent evaluation is the discipline most teams skip — and the one that decides whether your agent survives production. Here's how to test agents for correctness, reliability, memory, and failure modes before and after you ship.
06/29/2026 · Model Evaluation · 9 min read
How to Benchmark AI Agents on Your Own Tools (Not Just Leaderboards)
Public leaderboards won't tell you if a model can actually drive your tools. Here's how to build a lightweight, reproducible agentic eval against your own harness — and why local models are now in the running.
06/28/2026 · Model Evaluation · 9 min read
Prompt Injection in 2026: How to Actually Defend Your AI Agents
Prompt injection is still the #1 blocker to shipping AI agents. Here is what the attack really is, why a system prompt won't fix it, and the defense-in-depth patterns that hold up in practice.
06/28/2026 · Research · 9 min read
How to Evaluate AI Agents in 2026: Beyond Benchmark Saturation
Static leaderboards are saturating, so durable agent evaluation is shifting to stress-testing in simulated environments. A practical 2026 framework for measuring whether your AI agent is actually reliable.
06/27/2026 · Model Evaluation · 8 min read
How to Secure an AI Agent: Prompt Injection, Role Confusion, and Red-Teaming in 2026
In one week of June 2026, three independent sources reframed agent security — Willison's role-confusion model, the RIFT-Bench red-teaming benchmark, and the MosaicLeaks secret-leak demo. Here's how to secure an AI agent as a trust-boundary problem, not a string-filtering one.
06/25/2026 · Research · 9 min read
How to Evaluate an AI Agent: Tool-Use Capability and Data-Leakage Risk
A practical framework for evaluating AI agents on two axes that both decide production-readiness: can it do the job on your own tooling, and can it be trusted not to leak data?
06/22/2026 · Model Evaluation · 7 min read
GLM-5.2: The New Leader in Open-Weights LLMs for Long-Horizon Agents
GLM-5.2 ships under an MIT license with a 1M-token context and now tops the open-weights field for long-horizon coding and agent work. Here's the evidence, how it compares, and how to run it.
06/22/2026 · Model Evaluation · 7 min read
Can Your AI Agent Keep a Secret? A 2026 Guide to Agent Data Leakage and Real Evaluation
AI agent security is now an evaluation problem: agents can leak private data through ordinary-looking tool calls and still pass every leaderboard. Here's how data leakage happens, how to red-team it, and how to benchmark whether an agent is actually reliable on your own tools.
06/21/2026 · Model Evaluation · 11 min read
Can Your AI Agent Keep a Secret? Testing Agents for Data Leakage
Capability evals tell you if an agent is smart. They don't tell you whether it will leak the sensitive data it can see. Here's how to test AI agents for data leakage and secret-keeping — grounded in new research and a real-world one-click leak.
06/21/2026 · Model Evaluation · 9 min read
A2A Protocol Explained: How Agents Talk to Other Agents
A live community thread asking "is anyone using A2A?" shows builders are still orienting in the agent-protocol landscape. Here's a vendor-neutral explainer of what the A2A protocol is, the problem it solves, and how it fits alongside MCP — grounded in the official spec.
06/21/2026 · Research · 8 min read
How to Benchmark an LLM's Agentic Tool Use on Your Own Stack
Public leaderboards won't tell you if a model works with your tools. Here's a practical, repeatable methodology to benchmark agentic tool use on your own stack — and the failure modes to watch.
06/20/2026 · AI Tutorials · 9 min read
GLM-5.2 for AI Agents: Benchmarks and How It Compares for Long-Horizon Tasks
GLM-5.2 is a new MIT-licensed, 1M-context open-weights model explicitly tuned for long-horizon agentic work. We break down what's new, the benchmarks that matter for agents, and how to judge it for your own stack.
06/20/2026 · Model Evaluation · 9 min read
Can You Trust an AI Agent? Evaluating Reliability, Data Leakage, and Security
Agent trust isn't a vibe - it's measurable. A practical playbook for evaluating agent reliability, secret-leakage, and security, grounded in this week's benchmarks and a real one-click exploit.
06/20/2026 · Model Evaluation · 9 min read
GLM-5.2: The Open-Weights Model Built for Long-Horizon Agents
Z.ai's GLM-5.2 is an MIT-licensed open-weights LLM aimed squarely at long-horizon agent work. We break down what actually changed, how it benchmarks, and whether it can run your agents.
06/20/2026 · Model Evaluation · 8 min read
AI Agent Security in 2026: How Agents Leak Data and the Defenses That Stop It
AI agent security broke into the headlines in June 2026 with a one-click Copilot exploit and new research on silent data leaks. Here's the risk-to-defense map for anyone running agents in production.
06/19/2026 · Research · 9 min read