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.
AGENTS
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Weak Verifiers Create Misaligned AI Agent Behavior
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Weak verifier, weak improvement. If you measure “cleaner writing,” the agent may learn to sound polished. If you measure “more engagement,” it may learn clickbait. If you measure “passes tests,” it may learn to satisfy the test suite without solving the real problem. The
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Verifiers: The Key to Evaluating AI Model Performance
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That is where the verifier comes in. A verifier is whatever checks the work. It can be: test results human review a simulator a benchmark real user outcomes a second model judging the first model
The verifier is the thing that decides what gets kept. -
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 -
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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User-Friendly Agent Interaction in LangSmith Studio
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Interacting with the agent in the studio is the easiest way to get a user friendly UI! I can share it with others in my LangSmith org, and they can chat with it there
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Agent Deployment via MCP, A2A and Remote Graph API
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As part of this deployment, you can interact with the agent in several ways We expose the agent via MCP and A2A, as well as with as a "remote graph" API, which means you can interact with as you would a normal in-memory graph See streaming docs here: https://
docs.langchain.com/langsmith/stre
aming
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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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