Build Production AI Agents

Here is the plain version. RAG, LLMs & AI agents for Forward Deployed Engineers is a trio of skills that form an effective stack, where the language model is used for reasoning, the RAG provides grounding in the form of the company’s knowledge, and the AI agent makes the system take action.

A Forward Deployed Engineer builds

such a system within the business, making sure that the correct data and actions are used. This post explains the components, the value, and how to get started.

What Are RAG, LLMs & AI Agents for Forward Deployed Engineers?

RAG, LLMs & AI agents for Forward Deployed Engineers refers to the practice of using large language models, retrieval augmented generation, and AI agents to create production-grade solutions within the client’s system.

The LLM is used for its reasoning ability, RAG provides grounding in the form of the company’s knowledge, and the AI agent uses tools to take action and complete the task. Compared to an automation script, which follows predetermined instructions, the agent is more flexible and can update the solution if the initia

l approach fails.

A Forward Deployed Engine

er is a role that creates such solutions on-site. The engineer does not aim to create a solution that follows the perfect specifications but works with the client to observe the current process and build an improved version based on it.

Why Companies Want This  Stack

Most companies have a mountain of documents that are ignored as soon as they are created. This is information that the employees are supposed to use but spend hours searching for the correct passage.

Enterprise RAG can help, as the system extracts the relevant information and presents it to the LLM, which uses it to complete the task. An AI agent can then act on the information, for instance, by finding the relevant customer support ticket and adding a response or updating the CRM.

This approach helps to reduce the time spent on the task significantly, while also reducing the need for further manual interventions.

The same goes for other documents, such as policies, contracts, and product documentation. The value of such a system increases with the size of the data that the RAG system can parse.

My personal opinion is that the demo is always the easy part — the production-ready solution is where the project fails most often.

The data is messy, the users do not follow the expected pattern, and the initial specifications were created without considering potential issues.

This is why companies hire the Forward Deployed Engineer to deploy and refine the solution in production.

How the Pieces Fit Together

LLMs: the reasoning layer

Large language models are not perfect, as they do not have access to private documents and sometimes make up information.

While this is not an issue for chatbots, enterprise solutions need to ensure that the LLMs are only given access to relevant data and that the responses are checked for hallucination.

In the ideal scenario, the engineer would select the model and write the prompts to achieve the necessary outcome without overloading the system with information.

RAG: the memory layer

A RAG system has four components — the documents are split into smaller pieces, encoded, stored in a vector database, and then retrieved and passed to the LLM as context.

I believe that the first two steps define the result, as the data is either useful or not, and there is little that the model can do to improve the quality of the input.

A RAG system should also have reranking and citation features so that the end-users can verify the answers.

Agents: the action layer

An AI agent uses a set of tools to perform a task, following the plan, using the output of one tool as an input for the next until the goal is achieved.

Through the LLM API, the agent can interact with external systems, such as CRM, ticketing systems, or emails.

The tools should be limited to specific functions and given a clear goal to avoid failures. For complex tasks, multiple agents can work together in a pipeline.

An agent that only interacts with language models is a chatbot. Tools are what makes an agent take action, such as updating the CRM or sending an email.

The agent system should also have evaluation and guardrails, such as input and output sanitization and verification by a human moderator.

What You Gain

Using the three components together and having the Forward Deployed Engineer build the solution delivers significant benefits.

First, the system is created faster and better, as the engineer observes the current process and finds the strongest points.

One solution can power multiple teams if there is a common pattern, and the system will be built based on the full set of documents rather than a few samples.

The tested system fails less often, and the clean stack is easier to maintain and update. The feedback loop is also shorter, meaning that the changes can be deployed faster — ideally, after a Monday complaint, the update is released on Wednesday.

How to Get Started

Select one area where the current process is simple, repeatable, and has measurable outcomes, rather than an unclear and inconsistently followed procedure. Document the current process and build a minimal viable product on a small set of documents.

Create a test set of questions and track the accuracy of the system. As you add new components, such as AI agents and RAG, you will need to monitor not only accuracy but also cost and speed. Before launching the solution, make sure that there is a human moderator to review the results for particularly important cases.

One final note: measure the results. A successful project will demonstrate its value, and a failed experiment is still a learning opportunity.

One strong recommendation is to build a working application, however small, rather than focusing on the theory. In most interviews, a candidate can demonstrate their abilities better by showing a project, however small, rather than a certificate.

Learn It in Hyderabad

If you want to learn the concepts hands-on, our RAG, LLMs & AI agents course for Forward Deployed Engineers can help you gain the necessary skills.

At N3 FDE Training Institute, our expert faculty, Subbaraju sir, has 25 years of industry experience — and our projects allow you to build working applications in the areas of RAG, AI agents, and deployment.

Or you can learn more about the RAG, LLMs & AI agents for Forward Deployed Engineers course on our training page. Students from Ameerpet and across Hyderabad are welcome to join our job-ready course for AI engineers and build their projects with us.

Where to Go Next

The landscape of AI is changing rapidly, and the engineers who understand how to build production-ready solutions will always be in demand. Get in touch with us to learn more about the opportunities.

N3 FDE Training

Flat 515, Fifth Floor, Annapurna Block,
ADITYA ENCLAVE, Kumar Basti, Ameerpet,
Hyderabad, Telangana 500016, India
📞 Phone: 09032102017