Topic
All digests tagged AI Agents
Nubank accelerated agent deployment by 20x using simulations. By generating grounded, multi-turn synthetic data instead of relying on slow production traces, teams can rapidly test and derisk AI agents.
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Learn how Oracle built an AI skill to enforce database best practices, improving SQL generation from 95% to 100% success rate using automated reviews and iterative refinement.
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Most agent failures are harness failures, not model failures. The system must own state transitions, enforce ordered commits, and use a run receipt to prove actions were visible to the user.
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Forward Deployed Engineering is transforming into building autonomous 'Software Factories.' This process automates the entire signal-to-deploy pipeline using agent harnesses and measuring codebases for 'agent readiness' to achieve massive scale.
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AI success hinges on Forward Deployed Engineers mapping complex human workflows onto existing enterprise systems. The solution involves building agents on top of legacy platforms, using dependency graphs, and post-training models for context extraction.
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Forward Deployed Engineering (FDE) at Decagon is identical to product engineering. The goal is ensuring custom work compounds into a self-serve platform, scaling from bespoke deployments to generalized products.
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Discover SQL MCP Server: The open-source solution for secure data access. It provides a standardized API layer to connect AI agents and modern apps to your enterprise database, simplifying development and enforcing security.
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Learn how Tessl's Dark Factory ships 70% of its code autonomously using AI agents, sophisticated verifiers, and a robust orchestration layer.
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Llama.cpp and vLLM are two key engines for local LLMs. Llama.cpp optimizes for consumer hardware using quantization and GGUF files, while vLLM focuses on production scale with continuous batching and paged attention.
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Learn how Netflix built an AI agent to automate performance engineering. Discover how LLMs can read profiling data, find quadratic-time anti-patterns, and propose fixes validated by real production traffic.
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Harness engineering is a discipline focused on building automated loops of checks and maintenance agents to delegate more work to AI. This process involves improving three dimensions: autonomy, automation, and quality.
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Moving beyond simple output checks, effective AI agent evaluation requires measuring trajectory and skill activation. Learn how structured context improves code quality metrics like PR throughput and adherence to unique business IP.
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Docker introduces sandboxing using MicroVMs to safely run autonomous AI agents locally. This hardware-level isolation prevents rogue agents from accessing sensitive host data, balancing speed and security.
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To reliably evaluate AI agents, companies must move beyond simple production traces and build private, repeatable simulations. This process integrates benchmarking into the Agent Ops CI pipeline for continuous improvement.
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Arize's Signal automates debugging by pulling production traces into the repo as files. This allows AI agents to understand the exact code path, moving observability from dashboards to continuous, self-improving systems.
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Evals must evolve with agents: from static checks to LLM judges, the future requires Agent as a Judge for adaptive dynamic analysis.
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Learn how to move from token maxxing to value maxxing with GPT-5.6. Optimize your AI agents by leveraging programmatic tool calling, prompt caching, and model selection (Sol, Terra, Luna) for maximum cost efficiency.
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Harbor provides a rollout-centered view of evaluating and improving AI agents. It connects sandboxed environments, agent evaluations, and optimization workflows into a practical framework.
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Jason Liu details how AI agents, using 'Appshots' and custom skills, automate complex knowledge work—from managing Git PRs to running continuous project monitoring.
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AI agents require a 'data harness' optimized for conversational pace. This requires combining open standards (Iceberg, Arrow) with a semantic layer to ensure data is accessible, understandable, and performant.
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