Agentic AI and RAG: how to build applications that learn and reason

Artificial Intelligence is no longer limited to completing sentences or suggesting code snippets. With the evolution of large language models (LLM), the next step is to make them agents capable of interacting with external environments, consulting knowledge bases, and making autonomous decisions. This is what is now referred to as Agentic AI.

At the same time, companies have realized that a “pure” model is not sufficient to ensure reliable responses: it needs to be connected to updated knowledge bases. This is where the Retrieval Augmented Generation (RAG) paradigm comes into play, which combines search and generation to provide more accurate and verifiable outputs.

In this article, we analyze how they work, how they differ, and how we can integrate them to build applications that learn, reason, and adhere to compliance requirements.

An AI agent is not limited to responding: it can perform actions, monitor results, and adapt behavior.
Examples of agent capabilities:

• Access a database and run queries
• Interact with external APIs (e.g., schedule a call on Teams)
• Write code, test it, and commit to GitHub
• Decide whether to involve a human in case of ambiguity

The logic is no longer “input → output,” but perception–action–feedback loop, inspired by autonomous systems.

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What is Retrieval Augmented Generation (RAG)

The RAG addresses a known limitation of LLMs: the lack of updates and the tendency for “hallucinations.”
It works like this:

1. The user input is transformed into an embedding.
2. The embedding is compared with a vector database (Pinecone, Weaviate, Qdrant, Postgres+pgvector).
3. The most relevant documents are retrieved and passed to the model.
4. The LLM generates the response based on the sources.

The result is a more contextualized, citable, and reliable output.

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Differences between traditional RAG and Agentic AI

• RAG → enhances the external memory of the model. It retrieves data, but the LLM remains passive.
• Agentic AI → adds action capabilities. The agent can decide which tool to use, when to query the knowledge base, and how to orchestrate the steps.

👉 The combination of the two leads to powerful solutions: agents that consult updated sources, reason about the results, and act accordingly.

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Typical Architecture

An Agentic AI + RAG system includes:

• LLM core: the brain (e.g., GPT-4o, Claude, Llama).
• Agent orchestrator: LangChain, Semantic Kernel, LlamaIndex.
• Vector DB: embedding repository for retrieval.
• External toolset: API, database, Git repository, CRM.
• Governance layer: logging, monitoring, security controls.

This architecture supports both advanced chatbots and integrated enterprise applications.

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Concrete Use Cases

1. Chatbot with human escalation

An HR assistant answers employee questions. If it does not find the updated policy, it queries SharePoint (RAG). If the confidence is low, it automatically creates a ticket for the HR office (agentic action).

2. DevOps Automation

An agent monitors the CI/CD pipelines. When it detects a build error, it consults the internal documentation (RAG), proposes a patch, and opens a pull request on GitHub.

3. Document intelligence in regulated sectors

In banking or healthcare, an AI agent can extract clauses from contracts, verify them against updated regulations (RAG), and flag inconsistencies, with an auditable log.

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Challenges and Risks

• Residual hallucination: even with RAG, responses need to be verified.
• Security: an agent acting on external APIs must have strictly controlled permissions.
• Governance: audit, explainability, and complete logging policies are needed.
• Performance: orchestrating retrieval + agent can introduce latency.

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How to Test and Validate AI Agents

1. Unit tests on prompts and tools: validate individual functions.
2. End-to-end tests: simulate complete conversations.
3. Quality metrics: accuracy, retrieval precision, fallback rate.
4. Continuous evaluation: monitor in production with anonymous datasets.

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Best practices for adoption

• Start with a limited MVP (e.g., a single agent + one tool).
• Integrate guardrails: action limits, API whitelists.
• Document the entire flow: from retrieval to agent decisions.
• Involve end users in testing to gather real feedback.

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Conclusion

Agentic AI and RAG represent the new frontier of software development: no longer isolated models, but systems that combine up-to-date knowledge and action capabilities.
Those who can integrate them with governance and strategic vision will be able to achieve smarter, more reliable applications that are, above all, production-ready.

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➡️ Do you want to understand how to implement RAG and AI agents in your business projects? Contact me for targeted consulting.