Retrieval-Augmented Generation (RAG) is not dead—but it has fundamentally evolved. What began as a simple solution to overcome the limitations of LLM context windows has transformed into a more sophisticated discipline known as context engineering, driven by the rise of agentic AI systems. In the early days of generative AI, RAG emerged as a critical technique to inject relevant enterprise data into prompts at query time. By retrieving context from vector databases and combining it with LLM outputs, teams could deliver accurate, up-to-date responses without relying solely on model training data. Tools like LangChain and LlamaIndex made RAG accessible, and vector databases became standard infrastructure. However, the limitations of naive RAG quickly became apparent. Simply retrieving top-k vectors often led to context poisoning, distraction, or overload—especially when scaling to enterprise data volumes. Overwhelming models with irrelevant or contradictory information degraded performance, a phenomenon now widely recognized as context rot or context clash. Even with improvements like re-rankers, the original one-shot retrieval model struggled to meet the demands of complex, multi-step workflows. The shift to agentic AI has accelerated this evolution. Agents don’t just respond to prompts—they plan, reason, act, and remember. This requires dynamic, iterative context management. Instead of dumping all retrieved data into a prompt, modern systems now use context engineering: a strategic process of writing, compressing, isolating, and selecting context at each step of an agent’s reasoning journey. This new paradigm treats retrieval as just one tool among many. Agents may use relational lookups, file searches, semantic queries, or knowledge graph traversals depending on the task. As Mark Brooker of AWS noted, semantic retrieval is now one of many retrieval methods, alongside traditional database queries and indexing strategies. Knowledge graphs have emerged as a foundational element in this transformation. Unlike flat vector embeddings, they encode relationships between entities, enabling richer, more explainable reasoning. GraphRAG—popularized by Microsoft’s open-source framework—demonstrates how graph-based retrieval enhances accuracy and traceability. The recent wave of acquisitions in the knowledge graph space—Progress acquiring MarkLogic, Samsung buying Oxford Semantic Technologies, Ontotext and Semantic Web Company merging into Graphwise, and ServiceNow acquiring data.world—signals a broader recognition: structured, semantic data is essential for trustworthy AI. At the heart of this evolution is the semantic layer. A semantic layer provides a standardized, machine-readable interpretation of data across systems—relational, document-based, and unstructured. It ensures consistency in definitions, enables cross-domain reasoning, and supports governance. Initiatives like Snowflake’s Open Semantic Interchange (OSI) aim to standardize metadata for AI readiness. But true semantic layers must go beyond relational data to include the rich context of documents, images, audio, and video—leveraging decades of work in information science and the Semantic Web. Evaluation is also maturing. Frameworks like Ragas, LangSmith, and Databricks Mosaic AI now assess not just answer accuracy but also context relevance, groundedness, provenance, coverage, and recency. These metrics are critical for ensuring AI outputs are both correct and trustworthy. Equally important are policy guardrails. As AI agents access sensitive data, systems must enforce access control, compliance, and ethical guidelines. Tools like Open Policy Agent and Oso are embedding policy-as-code into agent workflows, ensuring retrieval respects regulations and organizational rules. Looking ahead, retrieval will become multimodal, spanning text, code, images, geospatial data, and more. Systems will combine multiple retrieval modes—vector, lexical, graph, and relational—using composite strategies. Agent memories and tool registries will be queryable, and metadata will govern not just data but also the tools and actions available to agents. In short, RAG’s legacy lives on—but in a new form. The future of AI is not retrieval alone, but intelligent, governed, and explainable context engineering. Knowledge graphs, semantic layers, and robust metadata management are no longer optional. They are the backbone of responsible, high-performance agentic systems. The next generation of AI won’t just know what to say—it will know how, when, and why to retrieve, reason, and act.