#agents

Skill cards, self-hosted sandboxes, MCP tunnels, computer-use verifiers, and a Five Eyes warning all landed in twenty-one days. The boring perimeter around capable agents finally has shape.

Twenty minutes of tabs vs. five minutes of prompt. The traditional web wasn't designed for humans — it was designed for mice. The agent-native web is quietly dismantling the parts that never made sense.

Six-hour agent runs are now real. The harness — checkpoints, durable state, recovery — matters more than the model. A field guide to the long-running pattern.

Anthropic just shipped 10 financial services agent templates. The interesting part isn't the templates — it's the three-layer architecture quietly becoming the standard for enterprise agents.

Anthropic shipped a lot on May 6 — Managed Agents updates, Dreaming, Outcomes, Multi-agent Orchestration, and a SpaceX partnership. The signal-to-noise filtered down to five things that change how you build.

Langfuse got acquired by ClickHouse. Helicone hit maintenance mode. OpenTelemetry standardized LLM tracing. The observability stack for agents reshuffled in three months. Here's what it looks like now.

Vibes-based scoring is finally dying. Trajectory eval, rubric eval, golden replay, and the test pyramid that production agent teams actually run.

Deep dive into voice agent architectures. Why cascaded models give you control and fused models trade complexity for naturalness. What we're learning from shipping production agents at scale.

Firecracker microVMs, gVisor containers, persistent workspaces, and the $24M Series A nobody quite expected. The sandbox layer beneath every serious agent — and how to pick the right one.

Why your cascaded voice agent sounds robotic — and how to fix it with concrete examples, SSML pause patterns, emotion tags, and personality-as-behavior prompting techniques.

Caching, routing, distillation, and per-task model selection. The four moves that take a $0.40/task agent to $0.04/task without anyone noticing the difference.

Operator died, Browser Use became the default substrate, Manus shipped at scale, and the gap between demo and reliable production narrowed considerably. A field report.

The three-role pattern that powered Manus, then LangChain Deep Agents, then half the production agents shipping in early 2026. Why it works, when it doesn't, and how to actually build one.

A deep dive into context engineering — the techniques that separate toy demos from production AI agents. Covers compaction, offloading, isolation, caching, and prioritization with real examples from Manus, Claude Code, and Devin.

How exploring LLM fine-tuning led to building a Gymnasium-compatible RL environment where 7 LLM-powered agents run a company — trained with GRPO + LoRA on Qwen 2.5 14B — and what we learned about reward design, emergent collaboration, and the future of agentic AI.

AI agents are drowning in tools. The more APIs you connect via MCP, the worse your agent performs. Here's why, and what Code Mode changes.

We've been so obsessed with what AI can do that we forgot about how it feels to use it. The AI experience layer is the next frontier — not the model, not the capabilities.

Past ~30 tools, agent reliability falls off a cliff. Past ~100, it's chaos. Here's the actual engineering — RAG-over-tools, semantic routing, dynamic loading, and namespacing — that production teams ship to stay sane.

The orchestrator-worker pattern that took over agent design in late 2025 is the same pattern that took over backend design in 2014. The wins are real. So are the failure modes.

A comprehensive hands-on comparison of seven open-source AI agent frameworks — which one should you actually use?

Six serious frameworks, four orchestration styles, and one tired question I keep getting asked. Here's the decision tree I actually use.

Fourteen months in, the Model Context Protocol stopped being a curiosity and started being plumbing. Here's what changed over the holidays — registries, governance, and the first scaling pains.

Open-sourcing my childhood dream — an AI agent that understands context, makes decisions, and controls connected devices just like JARVIS.