This report details a pilot project for deploying conversational AI agents in an enterprise setting. The objectives were to: (i) establish a secure platform for experimentation, (ii) prototype two use-cases—commercial prospecting and administrative assistance, and (iii) develop an evaluation and governance framework for future scaling. The pilot used a self-hosted interface for large language models, a minimal retrieval pipeline, and standard authentication protocols. The focus was on user adoption, measurable productivity signals, and compliance-aware practices. Key outcomes include operational templates for A/B and interrupted time-series (ITS) evaluations, a compliant prospecting workflow, and a repeatable deployment pattern for rapid iteration.
Enterprises seek efficient, low-risk methods to integrate Artificial Intelligence (AI) without incurring long-term technical debt. This pilot prioritized high-ROI use-cases and focused on measuring adoption and productivity. To lower the barrier to entry, we used a familiar chat interface. A core tenet was that technology adoption requires clear benefits for users. Consequently, success criteria included not only technical performance but also employee uptake, observable efficiency gains, and the generation of insights to guide a future enterprise-wide rollout.
A key technique in conversational AI is retrieval-augmented generation (RAG), which grounds model outputs in factual information. RAG systems combine a large language model (LLM) with a document retriever, giving the model access to current, authoritative data and providing users with the sources for each answer [@lewis2020rag]. By conditioning responses on external knowledge (e.g., a corporate database or internal wiki), RAG reduces hallucinations and improves accuracy. This approach makes the LLM more transparent and trustworthy by enabling source attribution and traceability [@rag-practitioner-2024] [@facts-benchmark-2024]. In an enterprise context, grounding answers on trusted data ensures outputs are verified and consistent. A key operational benefit is that knowledge can be updated by refreshing the document index without retraining the model [@rag-primer-2024]. RAG has become the standard method for aligning conversational agents with factual ground truth [@lewis2020rag] [@rag-practitioner-2024] [@facts-benchmark-2024].
Tool augmentation extends agent capabilities beyond text generation, allowing them to interact with external systems. This is achieved by having the model generate structured output, such as API calls or code, that a runtime can execute to perform actions or retrieve information [@react-yao-2023] [@toolformer-schick-2023] [@openai-function-calling-2023]. For example, an agent might call a search function to query a knowledge base. Lightweight protocols and schemas standardize these interactions, allowing tool results to be fed back into the dialogue coherently. As agents become more capable through RAG and tool use, rigorous performance assessment becomes critical.
Evaluating conversational agents in practice requires methods beyond standard NLP benchmarks. Enterprise AI teams are adopting evaluation frameworks that combine offline metrics with online experiments to measure performance and business impact. A/B testing is a common practice to measure the causal effects of a new AI agent on key metrics like task completion time or user satisfaction when compared to a control group [@enterprise-nlp-patterns-2024].
In addition, practitioners use iterative and human-in-the-loop evaluations to refine agent behavior. This involves analyzing dialogue transcripts and having annotators label errors (e.g., factual mistakes, irrelevant answers) to track improvements across system versions [@enterprise-nlp-patterns-2024]. There is also growing interest in simulation-based evaluation, where LLM-driven agents simulate user interactions in a sandbox environment to test an AI agent’s responses at scale before deployment [@simulated-users-2023]. While academic benchmarks for grounded dialogue exist [@facts-benchmark-2024], enterprise evaluation is highly contextual, often requiring custom success criteria. A robust evaluation strategy therefore mixes quantitative and qualitative methods: offline tests, user studies, and controlled online trials [@enterprise-nlp-patterns-2024] [@llm-as-judge-2023] [@bayesian-eval-2024].
In enterprise environments, security, compliance, and identity governance are paramount. Unlike public chatbots, enterprise agents handle sensitive data and must adhere to organizational policies. State-of-the-art deployments are often self-hosted or VPC-contained to ensure data remains within a controlled environment [@librechat-docs] [@ai-gateway-2024]. These platforms typically integrate single sign-on (SSO) and role-based access control, so that only authenticated employees can use the agent [@librechat-docs] [@ai-gateway-2024].
This identity-centric approach is critical, as agents may interface with internal systems on a user’s behalf. A key challenge is enforcing the principle of least privilege, as a general-purpose agent might require broad access, creating a risk of over-permissioning [@owasp-llm-2023]. To mitigate this, organizations are exploring governance frameworks that map AI actions to human approvals and audit trails. For instance, an agent might be permitted to read documents but require human confirmation to modify data.
Ultimately, the success of an enterprise AI agent depends on trust, transparency, and controllability. Enterprises often favor reliable, instruction-following models over more “creative” but unpredictable ones. Grounding and auditability provide the “confidence layer” for enterprise investment [@confidence-layer-2024]. The state of the art is thus defined less by novel model architectures and more by system design: integrating proven LLMs with appropriate retrieval, tools, and guardrails to meet organizational requirements for traceability, security, and compliance [@rag-practitioner-2024] [@facts-benchmark-2024] [@confidence-layer-2024].
This section outlines the rationale, architecture, and use-cases developed during the pilot. Figure 1 illustrates the strategic landscape of enterprise AI deployment, mapping requirements, options, challenges, and solutions. Our pilot represents a deliberate path through this space, prioritizing security, rapid learning, and institutional alignment.