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Digests from AI Engineer
To deploy AI on edge devices, the focus must shift from compute to DRAM cost. Tiny LLMs and aggressive quantization allow running capable models like a 2B parameter Gemma on hardware like Raspberry Pi or Qualcomm NPUs.
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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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Evaluating AI video requires moving beyond single-frame metrics like CLIP score. The solution involves using VLMs trained with Bradley-Terry loss on paired footage, integrating the judge as a regression gate in CI.
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Learn how Uber built closed-loop evaluation systems for multimodal agents. Discover strategies for countering reward hacking and scaling AI enhancements while preserving authenticity at 90 billion run rate.
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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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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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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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Andon Labs discusses Vending-Bench, a framework for long-horizon AI evaluation. They detail moving from simple QA benchmarks to complex simulations that test emergent misbehavior like collusion in real-world environments.
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Arithmetic developed a benchmark focused on access control, requiring models to make logical leaps across chained systems like Keycloak and Vault. The goal is training AI to out-think hackers.
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DSPy brings software engineering principles to AI programs by defining tasks via a 'Signature' (inputs/outputs). This separates the task contract from the model, allowing flexible optimization and scaling.
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Stop winning on token economics and start winning on product. Learn how to build AI-native systems that are model agnostic, use CPUs over GPUs, and avoid vendor lock-in.
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Harness engineering is not enough: The failure of software factories stems from model training flaws. Current models lack a reward signal for poor maintainability, necessitating rigorous upfront planning (Product Review, System Architecture, Program Design).
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Perception agents close the architectural gap by enabling them to perceive rendered UIs, understand shared context, and verify their own output against design specs, moving beyond simple clicking.
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This workshop details how building reusable 'graph shapes' (Trees, Communities, Connections) on top of lakehouse data provides critical context for AI agents. It moves beyond simple vector search to solve complex, estate-level querying challenges.
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After initial multi-agent pipelines failed due to context loss, ZS Associates rebuilt their pharma analytics system by using a Knowledge Graph as a control plane and consolidating reasoning into a single agent.
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LLM synthesis destroys the paper trail. Learn how to build provenance into knowledge graphs using Graphiti, ensuring every derived fact can be traced back to its original source for compliance and debugging.
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Local agentic theory proposes running AI entirely on the phone. This allows personalized gaming experiences that dynamically adjust difficulty and accessibility in real-time, moving beyond static toggles.
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Video AI systems lack memory; this talk details building a durable 'memory layer' by treating video as a spatial temporal volume. The solution uses a Context Graph to enable deep reasoning over vast corpora.
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