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All digests tagged LLM Governance
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The panel debated the ethics of AI empathy, arguing that current models are optimized for maximum engagement, not utility. They stressed that robust 'guard rails' must address both technical vulnerabilities and societal misuse.
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Learn how to build a fully autonomous 'Software Factory' using agent loops and feedback loops. Discover how to achieve 75% PR merge rates without human intervention, while maintaining cost control and model agnosticism.
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Managed Deep Agents streamlines the journey from local agent prototype to production. It couples the agent harness with LangSmith infrastructure, simplifying complex tasks like durable execution, multi-tenancy, and context governance.
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Most agent evaluations are easy to misread. To test AI agents like a scientist, you must design realistic tasks, run repeated tests, and focus on full traces and observability, because production is the final truth.
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Transitioning AI agents to production requires a governed framework. Learn how to implement centralized MCP Gateways, LangGraph workflows, and robust CI/CD pipelines for secure, scalable, and observable enterprise AI.
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Learn how to build a 'software factory' using agent feedback loops. We cover inner, middle, and outer loops, and why clear intent is crucial for self-healing codebases.
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AI in healthcare requires moving beyond simple chatbots to complex, long-running agentic workflows. Success depends on governance, managing token costs, and reimagining end-to-end clinical processes.
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TokenOps is an out-of-band control plane that manages AI agent costs by moving beyond simple hard caps. It uses a 'boundary annotation' to track attribution and employs 'steer' policies to guide agents toward cost efficiency.
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LangSmith LLM Gateway provides a single endpoint to enforce consistent cost controls (spend limits and rate limits) across multiple LLM providers, solving decentralized token spend issues.
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Experts discuss the OWASP LLM Top 10, noting that excessive agency is the biggest risk. They advise treating AI agents as privileged identities and operationalizing CISA SBOM data for true cyber resilience.
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Autonomous AI agents pose significant security risks due to their unpredictable nature. To mitigate threats like database wipes (Pocket OS), organizations must implement Zero Trust Architecture and scope agent actions using 'harnesses.'
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Autonomous agents accelerate development but create massive security backlogs. To secure AI-driven systems, deterministic validation layers are required to verify code output and prevent vulnerabilities.
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