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All digests tagged Agent Development
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Building self-improving agents requires understanding three parts: Model, Harness, and Context. The foundation for improvement is robust observability via Traces and Evals.
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Continual learning is becoming boring, in a good way: it is the software development life cycle, applied to models. Model development requires systematic practices like versioning, APIs, and robust evaluation (Evals).
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This workshop details how to master AI Observability by building a continuous agent improvement flywheel. Using Braintrust, engineers can derive signal from production traces via custom scoring and Topics, automating the process of identifying failures and proposing code changes.
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Jan Čurn argues that MCP criticism stems from naive client implementations, not the protocol. He details fixes like progressive tool discovery and Code Mode, advocating for MCP for remote access and CLIs for local agent interaction.
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Discover CoreWeave ARIA, an AI research agent built into Coreweave Forge. ARIA automates the AI development lifecycle by analyzing metrics, generating comparative workspace views, and running auto research experiments to accelerate model optimization.
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Jev, a new System 1 model from TypeSafe AI, is designed for fast, structured decision-making. It is up to 200x faster and 400x cheaper than LLMs for classification tasks, making it ideal for model routing and agent evaluation.
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CoreWeave ARIA is an AI research agent built into Weights & Biases that accelerates the self-improving AI loop. It automates auto-research, analyzes training runs, and optimizes agent performance.
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LangSmith Preview Builds bring standard software development workflows to AI agents. By deploying a temporary environment for every pull request, teams can test agent changes safely before they reach production.
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Learn how to add custom tools to your managed deep agent using LangChain. Tools allow agents to interact with external APIs and databases by defining them as decorated Python functions.
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Skills package task-specific procedures and context into reusable directories. You can define them in markdown files, and they are picked up automatically by the agent.
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Building reliable AI agents requires a comprehensive evaluation loop that extends far beyond prompt engineering. The process involves optimizing foundational tools, establishing rigorous evaluation workflows (Evals), and iteratively refining behavior using feedback signals.
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Learn how to boost voice agent transcription accuracy with Agent Context Carryover. This feature automatically feeds conversational context into LiveKit's Universal 3.5 Pro model, simplifying complex AI integrations.
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