So the practical question for builders is not: “How do I make my agent more autonomous?” It is: “What feedback loop am I letting it optimize?” Because once the loop starts running, your verifier becomes the steering wheel.
PROMPT ENGINEERING
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AI Generation Quality Assessment Beyond Candidate Quantity
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Generating candidates is now easy compared to back then. An AI can generate: 100 prompt variants 50 code changes 20 tool-routing ideas 10 eval rewrites 5 new workflows
The question is not “can it come up with changes?”
The question is “which changes are actually better?” -
Karpathy’s AutoResearch: 700 Experiments, 20 Key Optimizations Found
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Karpathy’s AutoResearch made this concrete. One markdown prompt. 630 lines of training code. One GPU. 2 days.
It ran 700 experiments and found 20 training optimizations.
Most people was impressed by on the 700 experiments.
But what most people missed out is what decided which 20 -
Error Handling and Retry Mechanisms in AI Agent Blocks
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Good question. Sim has retries and error paths built into each block, so you can set retry counts and route failures to a fallback branch. For the agent blocks, I also keep prompts strict on output format so downstream parsing doesn't break. Haven't needed heavy fallback
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Agent Environment Architecture: Memory Routing Integration
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Thanks! That framing is exactly how I started seeing it too. Once memory, routing, and multi-channel sit in one place, the workflow stops feeling like a script and starts feeling like an environment the agents actually live in.
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AI-Generated Workflow Structures and Intelligent Branching Logic
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Yeah, Mothership generated the structure from a prompt. For branching, it picks the right block (router for classification, condition for if/else, parallel for fan-out) and wires them up. I usually tweak the routes and prompts after, but the skeleton holds up surprisingly well.
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Deep Agents Launches Per-Model Harness Profiles Feature
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Until today, Deep Agents shipped with a single set of prompts, tools, and middleware aimed to work well across all Large Language Models. With the launch of harness profiles, you can now control these parameters on a per-model basis.
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9 Steps to Build Functional AI Agents from Scratch
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How to build AI agents from scratch (9 steps): 1. Purpose & scope 2. I/O schemas 3. System instructions 4. Reasoning + tools 5. Multi-agent orchestration 6. Memory & context 7. Multimodal 8. Structured outputs 9. UI / API Ship agents that do work, not just talk.
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Deep Agents: Optimizing Performance Across Model Families
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great deep dive into how we get deepagents to work well with different families of models
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Claude Usage: Understanding Limitations and Smart Optimization
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The people who say Claude is too restrictive are mostly running 30-message Opus chats with web search on while uploading the same PDF repeatedly. The people who use it all day are not using it less. They are using it smarter. Save this. The next time you hit a limit, the fix is