Rastox | Blog: Agentic AI

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The Necessity of Agentic Observability: Moving Beyond LLM Metrics

20 November 2025 | By rastox_team

Why traditional monitoring fails in autonomous agentic environments. We explore the need for Continuous Evals (CE) and system-level tracing to ensure reliability, safety, and business alignment in complex AI systems.

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Cutting Costs, Not Capabilities: Strategic LLM Optimization for the Enterprise

15 October 2025 | By rastox_ops

A deep dive into prompt engineering techniques, smart caching strategies, and model selection criteria that significantly reduce inference costs without sacrificing the performance or intelligence of your enterprise AI agents.

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Building from the Ground Up: Why Custom AI Architecture Beats Off-the-Shelf Solutions

01 September 2025 | By rastox_arch

An argument for tailored AI solutions. Learn how matching AI architecture (RAG, vector stores, custom agents) directly to specific business processes delivers superior ROI compared to generic, one-size-fits-all platforms.

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RAG 2.0: Building Reliable Retrieval Systems for Autonomous Agents

05 June 2025 | By rastox_data

Moving past simple vector search. We discuss advanced Retrieval-Augmented Generation (RAG) techniques, including hybrid search and multi-step reasoning, to ensure your AI agents access and synthesize the most accurate data for critical tasks.

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Governing the Agent: Mitigating Risk in Self-Driving AI Workflows

18 June 2025 | By rastox_gov

As agents gain autonomy, the risk profile changes. This article outlines essential strategies for AI governance, implementing strict safety guardrails, and setting transparent decision boundaries to prevent unintended consequences in production.

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The Shift from Pipelines to Policy: Why Traditional ML Teams Need Agentic Skills

10 July 2024 | By rastox_strategy

The move from batch-processing data pipelines to managing real-time, policy-driven agents requires a fundamental change in team structure and expertise. We detail the new roles and skills required to effectively operate agentic AI at scale.