That is the real lesson. The next wave of AI products will not just use better models. They will improve their own prompts, tools, memory, evals, and workflows. But the teams that win will not be the ones with the most autonomous agents. They will be the ones with the best
AUTOMATION
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Start Small: Iterative Agent Development Best Practice
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The useful version starts small. Do not let the agent rewrite everything. Let it improve one narrow part: one prompt one tool call one retrieval step one eval one memory rule one workflow
Then test the result against something real. -
Agent Autonomy: Design Through Feedback Loop Verification
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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.
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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?” -
The Verification Problem in AI Self-Improvement Systems
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AI agents can now generate endless ways to improve themselves. New prompts. New code. New plans. New experiments. New tool calls.
That is not the bottleneck anymore.
The bottleneck is the verifier.
How do you know the new version is actually better? -
Building and Deploying Agents with DeepAgents Deploy
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That's a quick tour through building and deploying a simple agent with DeepAgents deploy Was just a markdown file, a json file, and toml file Easy to set up, but robust deployment by the end Try out deepagents deploy! Full docs here:
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Deploy AI Agents to LangSmith Using deepagents and Docker
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After that, it's go time! `deepagents deploy` This will use Docker under the hood, so make sure you have that installed It will build an agent server image, and then deploy it to LangSmith
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Building Documentation Assistant Agent Core Instructions
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Next up – the agents core instructions These live in AGENTS.md. I'm going to write some instructions for how this agent can be the best documentation assistant agent! You can see the full text here: https://
github.com/langchain-ai/d
eepagents/blob/main/examples/deploy-mcp-docs-agent/AGENTS.md
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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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Removing SOC Blockers to Maximize Engineering Team Performance
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Christina and co on a legendary run. (Don't SOC-block your engineers)