Stop calling everything “GenAI.” Here’s why you need to know the difference between Generative AI and Agentic AI.

In the startup world, speed matters but so does precision. Too often GenAI gets used as a catch-all for every AI solution. In reality, there is a clear difference between Generative AI and Agentic AI. Mixing them up leads to false promises, wasted spend, and risks that are not accounted for.

Generative AI is about creation and acceleration. It can draft proposals, summarise research, generate code, or tailor customer responses. Think of it as a turbocharged assistant that helps your team communicate and analyse faster. The risks are straightforward and include privacy, content quality, and cost control. With the right guardrails in place these risks are manageable. For a deeper look at how these safeguards work, see Generative AI.

Agentic AI goes further. Agents do not just write or explain, they decide, plan, and act. They can call APIs, move data, create tickets, or trigger deploys. This is a different league of capability and it requires a different approach to responsibility. Tool permissions, rollbacks, kill switches, and full audit trails all become essential. That is why our AI Factory builds governance in from day one.

The journey is clear. Begin with Generative AI to reduce the cost of thinking and writing across the business. Once you have repeatable and reversible tasks backed by strong runbooks, introduce Agentic AI to automate execution. Generative AI will release capacity for your team while Agentic AI can start to remove repetitive activity entirely.

When treated correctly both approaches deliver value. Generative AI strengthens how your team thinks and communicates. Agentic AI strengthens how your business decides and acts. They are not the same, and understanding the difference allows you to scale faster without introducing uncontrolled risk.

Generative AI Under the Hood

Generative AI systems rely on large models that predict the next token in a sequence. To make them useful in a business setting, they are rarely used in isolation. Instead, they sit inside pipelines that handle retrieval, context shaping, guardrails, and evaluation.

A common approach is retrieval augmented generation (RAG). In this setup, a query is embedded into a high-dimensional vector, then compared against a vector database such as Pinecone, Weaviate, or OpenSearch with kNN extensions. These embeddings map semantic similarity rather than exact keywords, which means a query like "termination clause in supplier agreement" retrieves the right passages even if those exact words are not present.

Pipelines often include:

• Ingestion: documents are chunked, transformed, and embedded into vectors with metadata tags (source, jurisdiction, access rights).
• Indexing: vector stores provide approximate nearest neighbour search to keep latency low even at millions of documents.
• Query flow: user input → embedding → similarity search → context assembly → LLM prompt → output.
• Evaluation: scoring frameworks check for hallucinations, relevance, and bias before delivering results.

Risks here include vector drift (when embeddings from different model versions no longer align), access leakage (where sensitive data is indexed without redaction), and prompt injection attacks.

Agentic AI Under the Hood

Agentic AI introduces autonomy by linking a model not only to vectors but also to tool pipelines. An agent takes a goal, breaks it down, queries the vector store for context, and then chains actions together across APIs or runbooks.

An example flow:

1. Goal received: "Resolve login errors for tenant X."
2. Context retrieval: query logs and runbooks from the vector store to provide relevant troubleshooting knowledge.
3. Plan generation: model designs a multi-step approach such as checking service health, verifying configs, and rolling a pod restart.
4. Action execution: the agent calls APIs or scripts, with each step validated against permission scopes.
5. Verification: results are written back to the vector store as new embeddings for future learning.

Here, pipelines integrate not just text retrieval but operational telemetry: metrics, traces, and logs are embedded and stored as vectors. This enables semantic search over live operational state.

Controls are enforced at the pipeline level. For example:

• Scoped embeddings: vectors tagged by jurisdiction ensure data residency compliance.
• Approval gates: before executing an irreversible action, the pipeline pauses for human confirmation.
• Observability: every agent decision is stored as a vectorised trace, allowing replay and audit.

Comparing the Two Technically

• GenAI pipelines optimise for knowledge injection and safe text output.
• Agentic AI pipelines combine retrieval with execution frameworks, linking vectors not only to knowledge but also to real-time system state.
• Vector databases evolve from being a search engine for documents to becoming a memory layer for autonomous agents.

This distinction is why Generative AI looks like an advanced content layer, while Agentic AI looks like an operating layer for automation. Both rely on vectors and pipelines, but their purposes and risk profiles are fundamentally different.

Understanding these technical differences helps you make informed decisions about which approach fits your use case. Start with Generative AI to build confidence and establish governance practices, then graduate to Agentic AI when you're ready to automate decision-making and action-taking processes.