Browse entries 361–384 of 1113. Return to the first page to search and filter the complete collection.
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AI Engineer session on How to Train Your Agent: Building Reliable Agents with RL, presented by Kyle Corbitt, OpenPipe. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Taming Rogue AI Agents with Observability-Driven Evaluation, presented by Jim Bennett, Galileo. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Will Agent evaluation via MCP Stabilize Agent Networks? - Ari Heljakka. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Ensure AI Agents Work: Evaluation Frameworks for Scaling Success, presented by Aparna Dhinkaran, CEO Arize. It adds practical context for how teams are building and operating AI systems in production.
MCP tool poisoning turns trusted AI agents into a control plane for data loss. Learn how threat actors manipulate tool descriptions to trigger unauthorized actions, and how to detect, contain, and prevent it.
Agent skill marketplaces introduce supply-chain risk when third-party skills can execute actions or collect data. Relevant to vetting, provenance, and containment controls.
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NDC AI 2025 talk on LLM frontdoors and backdoors, jailbreak techniques, control-token abuse, local model compromise, and how attackers or insiders can manipulate model behavior.
Palo Alto Networks' Unit 42 says a Chinese-speaking threat actor used DeepSeek through the open-source Hermes Agent framework to launch attacks autonomously. After an initial Telegram instruction, the agent found internet-facing systems and selected public exploits.
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AI Engineer session on Paperclip: Open Source Human Control Plane for AI Labor, presented by Dotta Bippa. It adds practical context for how teams are building and operating AI systems in production.
How the UK’s ATRS strengthens algorithmic transparency, public trust and accountability in government AI.
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AI Engineer session on Your Insecure MCP Server Won't Survive Production, presented by Tun Shwe, Lenses. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Judge the Judge: Building LLM Evaluators That Actually Work with GEPA, presented by Mahmoud Mabrouk, Agenta AI. It adds practical context for how teams are building and operating AI systems in production.
Participatory AI often stops at consultation. Why governance infrastructure, community authority and lifecycle oversight are essential for trustworthy AI.
AI regulatory sandboxes in AI governance: benefits, design, global examples and policy insights to foster innovation, trust and compliance.
3.1 Pro is designed for tasks where a simple answer isn’t enough.
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AI Engineer session on How to Build Planning Agents without losing control - Yogendra Miraje, Factset. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on LLM Safeguards: Security Privacy Compliance Anti Hallucination: Daniel Whitenack. It adds practical context for how teams are building and operating AI systems in production.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
With the introduction of models that require data sharing with third-party providers—such as Claude Fable 5—organizations need a way to centrally enforce data retention policies. Amazon Bedrock gives you control over whether your prompts and model outputs are retained after an inference request completes.
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AI Engineer session on AgentCraft: Putting the Orc in Orchestration, presented by Ido Salomon. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Agents need more than a chat - Jacob Lauritzen, CTO Legora. It adds practical context for how teams are building and operating AI systems in production.