LeadGet Entrepreneur Boosts Conversion by 60%: How a Multi-Agent System Processes Leads

Six months ago, an entrepreneur launched his first AI agent, which successfully handled incoming applications and communicated with clients. Encouraged by the results, he added a second, then a third. But by the fourth, something broke: agents started getting confused, sending irrelevant data, and creating chaos. After restructuring into a multi-agent system, LeadGet achieved a 60% conversion rate, and in real estate, 2 out of 3 deals.

When a single AI agent successfully handles one task, there's a temptation to assign it more. But every language model has its limits, a "context window." Trying to grasp too much, the agent starts confusing data, losing details, and making errors. It's like asking an accountant to manage payroll, contracts, taxes, and HR simultaneously—eventually, they'll make mistakes, not because they’re bad, but because the volume exceeds their capacity to keep everything in mind. This pain is familiar to many, but a solution exists.

How Scaling AI Agents Led to Chaos

Initially, AI agents performed their narrow tasks perfectly: one processed applications and stored data in CRM, another handled documents, and a third performed video analytics. The problem arose when tasks became too numerous and no longer clearly defined. Agents began to "hallucinate," sending unfinalized estimates to clients, and analyzing documents related to other projects. This wasn't a model failure, but a systemic issue: a lack of architecture during scaling.

A single agent works effectively with one clearly defined task: writing posts, processing applications, summarizing meetings. But as soon as we try to assign it more, it becomes overloaded. The language model's context window is not infinite, and when handling several unrelated tasks simultaneously, the context becomes blurred, data gets confused, and accuracy drops.

Why a Single Agent Fails with Complex Tasks

Different tasks require different tools and approaches. An agent writing commercial proposals must deeply understand the product, customer base, and be persuasive. An agent analyzing contracts deals with legal terms and identifies risks. Attempting to create a universal agent leads to compromises that satisfy neither side. It's like trying to cover both tasks with a single prompt—the result will be mediocre for both.

Thus, it became clear that it wasn't just about adding more agents, but about creating a system where each agent specializes in its area, and their work is coordinated. This is how the entrepreneur arrived at multi-agent systems, which, according to Sber and the FinTech Association, will be the top technology trend of 2026.

How LeadGet's Multi-Agent System Was Designed

The solution involved dividing tasks among specialized agents and adding a managing agent—an orchestrator. The orchestrator receives the overall task, breaks it down into steps, and distributes them among the necessary sub-agents. It doesn't perform the work itself, but coordinates the process.

For LeadGet, Dmitry, one of the club's experts, built a team of four sub-agents:

• Engagement. Responsible for the first client contact.
• Qualifier. Handles lead qualification.
• Support. Provides customer support.
• Lead Detection. Identifies new requests.

The managing agent receives an incoming message and decides which sub-agent to forward it to. Each sub-agent has its own set of tools: some access external databases, others call a live manager, and some send webhooks to CRM. This architecture allows each agent to keep its context clean and work on its specialized task.

Implementation and Overcoming Challenges

The implementation process was not without its pitfalls. In the initial stages, when attempting to quickly combine several agents, "hallucinations" occurred—agents invented data and confused information between projects. An error at the first step multiplied throughout the chain. Roman from the AI Practiq Club, a builder, faced a similar problem when automating his PTO department.

The main lesson: debug each agent individually. Launch the first one, ensure its stable operation on real tasks, and only then add the second. Link agents one by one, testing each transition.

Other challenges included:

• Looping. An agent might encounter an obstacle (e.g., a blocked website or an unresponsive API) and enter an infinite retry loop, burning through API limits. Solution: explicit rules in the system prompt, limiting the number of attempts (e.g., no more than 3 per operation).
• Over-autonomy. An agent, given a broad task, might interpret it too broadly (e.g., adding a threat of legal action to a supplier email). Solution: AGENTS.md or a system prompt explicitly forbidding actions without confirmation. The basic rule: "Ask → confirm → act."
• Inefficient costs. Using expensive models for all tasks led to hundreds of dollars in bills for routine operations. Solution: match models to tasks. For simple operations, more economical Sonnet or Haiku might suffice; for complex ones, Opus. Some routine tasks can be reverted to simple bash scripts without AI.

Roman, for example, used the Gemini model for video analytics and Claude and Perplexity for documents, selecting optimal solutions for specific functions.

Results for LeadGet and Roman's Construction Company

The implementation of the multi-agent system brought impressive results for LeadGet and its clients:

• Conversion. 60% of deals for LeadGet's clients go through agents. In real estate, it's 2 out of 3.

In Roman's construction company, where AI agents were implemented in stages:

Roman is now building the final block—a system for participating in tenders, which will autonomously register on platforms, analyze technical requirements, prepare documents, and even analyze reasons for losing to adjust subsequent applications.

How to Implement This in Your Business

If you have complex processes that require coordination of multiple tasks, a multi-agent system is the next logical step. Here's how to start:

• Start with one agent. Always launch the first agent on a real task, ensure its stable operation, and only then add the second. Trying to build a complex system all at once often leads to chaos.
• Divide tasks. Separate incoming communication from data processing. The first agent qualifies requests, the second performs what is requested (writes proposals, searches for information, prepares documents).
• Define rules explicitly. Specify agent behavior rules in an AGENTS.md file or system prompt. For example: "Do not send emails without explicit confirmation," "Maximum 3 attempts per action," "If an error occurs, stop and report."
• Match models to functions. Don't use one expensive model for all tasks. More economical Sonnet or Haiku are suitable for simple operations, while Opus is for complex ones. Some routine tasks can be reverted to regular scripts.
• Use orchestration tools. For visual, no-code orchestration, n8n is suitable; for more complex development and custom prompts, Claude Code.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager