agent-infrastructure

Agent Skills and AGENTS.md: How to Organize What Your AI Agent Can Do

Agent skills are converging on a shared convention built around AGENTS.md and agent-optimized CLIs. Here's what that means, why how you organize skills changes runtime behavior, and how to structure agent tooling so the right capability fires at the right time.

06/13/2026 · AI Tutorials · 8 min read

Building a Reliable AI Agent Framework in 2026: Apache Burr and the Agent-Native Tooling Stack

Choosing a reliable AI agent framework now matters more than raw capability. Here's how Apache Burr's state-machine model approaches reliability, how it compares to LangGraph, and where agent-native tooling fits.

06/13/2026 · AI Tutorials · 10 min read

Multi-Agent Systems Risks: The Guardrails and Cost Controls That Keep Autonomous Agents in Check

Multi-agent systems risks shift from "wrong answer" to emergent, expensive, hard-to-supervise behavior once agents start interacting at scale. Here are the guardrails, cost controls, and observability that keep autonomous agents in check.

06/13/2026 · Industry Trends · 9 min read

Multi-Agent AI Risk: Why Agents Run Amok and How to Contain Them

When autonomous agents act on the world — and interact with each other at scale — small failures compound fast. Here are the real failure modes and the guardrail patterns that actually contain them.

06/11/2026 · Research · 8 min read

Context Engineering for Agents: Why More Memory Can Make Your AI Agent Worse

New research converges on a counterintuitive point: more memory can make AI agents worse. Here's what context engineering for agents means and how to manage agent memory well.

06/11/2026 · Research · 7 min read

Context Engineering for AI Agents: Why Less Context Beats More Memory

Context engineering for AI agents is having a moment: a wave of same-day research argues that curated, time-aware context beats piling on more memory. Here's what changed and how to design agents that stay accurate over long tasks.

06/10/2026 · Research · 8 min read

How Agent Environments Are Standardizing: OpenEnv, AGENTS.md, and Automation-as-Code

In a single week, three signals — OpenEnv, the AGENTS.md convention, and browser automations-as-code — pointed the same direction: AI agent infrastructure is converging on shared standards. Here's a practitioner's map of the emerging stack.

06/09/2026 · Research · 9 min read

Agentic RL Explained: What OpenEnv Means for Training AI Agents

Agentic RL trains AI agents by letting them act in environments and learn from outcomes. OpenEnv, backed by Hugging Face, PyTorch, Nvidia and more, gives open source the shared training substrate frontier labs already had.

06/09/2026 · Research · 10 min read

LLM Token Cost Optimization: How to Cut Your AI Agent Bill Without Cutting Quality

LLM token cost optimization is now the dominant operational story for teams running AI agents. Here's a practical playbook — caching, context trimming, model routing, batching, and budgets — to cut your token bill without gutting quality.

06/08/2026 · AI Tutorials · 9 min read

What OpenAI's Lockdown Mode Means for Prompt Injection Protection — And How to Actually Defend AI Agents

OpenAI shipped Lockdown Mode, the first named defense against prompt injection from a major lab. Here's what it does, what it doesn't, and the layered prompt injection protection that keeps any tool-using agent safe.

06/08/2026 · Research · 9 min read

How to Reduce AI Coding Agent Costs Without Slowing Your Team Down

AI coding agent bills have become a board-level line item, and some companies are already capping usage. Here are the levers — model routing, caching, scoped budgets, and observability — that cut spend without killing developer velocity.

06/07/2026 · AI Tutorials · 9 min read