LLM-Powered AI Agent Development: From Concept to Deployment

When an LLM Wrapper Falls Short

You’ve integrated GPT into support, but users complain: the bot gives generic answers, can’t perform concrete actions—check order status, modify a contract, approve a document. Sound familiar? We’ve encountered this dozens of times. The problem isn’t the model—it’s the architecture. A typical chatbot can’t plan or use tools. You need an AI agent that doesn’t just chat but actually does work: parses documents, calls APIs, executes SQL queries, and sends emails. Such an agent processes requests 50x faster than a human and reduces manual entry errors by 90%.

What Is LLM-Based AI Agent Development?

Developing an AI agent on top of an LLM means building a software module that uses a large language model as its reasoning and planning core, but can also call external functions (tools), access memory, and execute multi-step scenarios. Unlike a regular chatbot, an LLM agent can interact with corporate systems: CRM, ERP, databases, and APIs. The architecture includes: an LLM runtime (e.g., GPT-4o), an orchestration framework (LangChain), a context store (ChromaDB), and a set of tools described via OpenAPI or JSON Schema.

Why an AI Agent Outperforms a Standard Chatbot

A chatbot works on a question-answer basis: it receives a query and generates a response without using external data. An AI agent acts like an experienced assistant: it analyzes the task, breaks it into steps, calls needed tools (SQL, API, search), remembers history, and makes decisions. Thanks to the ReAct pattern (Reasoning and Acting), the agent can independently execute multi-step operations—for example, check warehouse stock, compare supplier prices, and create a purchase requisition.

Problems We Solve

Infinite Reasoning Loops

An agent can get stuck in a loop: think, call a tool, get a result, think again—until tokens run out. The cause is a lack of guardrails. We introduce a maximum number of iterations (usually 10–15) and a deadlock detector that breaks the cycle and returns control to the user.

Hallucinations When Calling Tools

The model might pass invalid parameters to a tool: for example, request a DELETE instead of a SELECT. Solution—strict parameter schema via JSON Schema and server-side validation. We also use few-shot examples of correct calls and set temperature to 0.2 to reduce creativity.

Long-Term Memory Issues

Standard LLM context is limited. The agent forgets what it did 10 steps ago. We combine short-term memory (last N messages), long-term memory (history summarization), and semantic memory (RAG on vector storage of key facts). This allows the agent to handle sessions thousands of steps long.

How We Design the Agent: Stack and Configs

Base stack: LangChain (ReAct loop), OpenAI GPT-4o (reasoning), ChromaDB (semantic memory), pgvector (integration with existing DB). For safety—tool execution isolation via Docker containers.

Example system prompt to reduce hallucinations:

You are a corporate AI assistant. Your task is to execute user requests using available tools.
Rules:
1. Never fabricate tool results. If a tool returns an error, report it.
2. If unsure which tool to use, ask the user for clarification.
3. Plan no more than 5 steps. If the task isn't solved, offer to hand over to a human.

We also tune hyperparameters: temperature=0.2 (less creativity, more accuracy), top_p=0.9. For financial agents—temperature=0.

For specialized tasks, we fine-tune the agent on corporate data—this improves accuracy to 95%. The agent uses RAG to access documents, enabling it to answer questions about internal regulations without hallucinations.

Case Study: Procurement Agent (From Our Practice)

One of our clients, a large retailer, suffered from slow procurement request processing. An employee filled out a form, then emails, approvals, supplier searches—took up to 5 days. We built an AI agent with tools: check_budget, search_suppliers, generate_contract_draft. Over 3 months of operation:

• Processing time dropped from 4.5 days to 2.1 hours (50x faster)
• 68% of requests handled without human intervention
• Error rate (wrong supplier/budget overrun) only 4%
• Savings of 800,000 ₽ per month, ROI of 300% over six months

The agent works with Jira: after preparing documents, it creates a task for approval. Financial operations are strictly forbidden—only preparation.

Work Process

We apply MLOps practices: data drift monitoring, automatic agent retraining, model versioning with MLflow.

How We Ensure Agent Security?

Security is key for corporate agents. We implement guardrails at multiple levels: input parameter validation, code execution isolation in sandbox containers, audit of all agent actions, and prohibition of critical operations without human confirmation. We also use chain-of-thought reasoning to improve decision transparency.

How to Avoid Mistakes in Agent Development?

1. Lack of tool call caching — repeated API requests. Solution: embed a TTL cache at the agent level.
2. Ignoring tool errors — the model continues despite an error. Solution: mandatory status check and return control to the user.
3. Overly long context window — after 20 steps the model loses focus. Solution: history summarization and semantic memory based on RAG.

Timelines and Cost

Cost is individually calculated—depends on number of tools, integration complexity, and security requirements. Typical savings from deployment reach hundreds of thousands of rubles monthly, with project ROI up to 300% in six months. Contact us—we'll assess your project in 2 days.

Our Experience and Guarantees

Over 10 years in AI/ML, 40+ projects, a team of certified AWS and GCP engineers. We guarantee quality: every agent undergoes load testing and code review. You get a fully turnkey solution with documentation and training. Order a turnkey AI agent—reach out for a consultation.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.