Multi-Agent AI Systems
I build multi-agent AI systems: several specialized agents tied together through orchestration, RAG and observability into one agentic workflow running on real client data.
A multi-agent system is an architecture where several specialized AI agents work together on one process, instead of a single agent trying to do everything. One agent reads documents and retrieves information, another executes actions in the client's systems, a third checks the quality of the output. An orchestration layer sits on top, assigning tasks, passing context between agents, and deciding what happens next. This approach fits processes with multiple steps that touch several systems at once - CRM, ERP, knowledge bases, customer communication.
I design and build these systems end to end, from architecture to production: orchestration, subagents, evals and observability, RAG and vector databases. I'm personally accountable for the result - one person, full ownership. On larger builds I bring in a team: I also run a five-person company, Infinity Tech Group, and a studio, ClawLabs, so extra hands are available when a project needs them, without losing control over the architecture.
A single AI agent handles one job well: it takes in a request, searches documents for an answer, performs one action in a system. Once a process has more steps - checking data in several places, making a decision, generating a document, updating three systems - a single agent starts to struggle, or does everything slowly and imprecisely.
A multi-agent system splits that work across specialized subagents, each with a narrower scope and its own tools. An orchestrator sits above them, assigns tasks, keeps the order straight, and merges the results into one response. It's the same agentic workflow, just broken into pieces that are easier to test, control, and extend separately.
The orchestration layer decides which subagent handles which piece of a task, in what order, and what happens when one of them fails. Underneath sits tool use - agents call actual functions and APIs rather than just generating text - and the MCP protocol, which connects agents to tools and client data in one repeatable way.
For retrieval I use RAG and vector databases, often in a hybrid setup that combines semantic search with classic keyword matching (BM25) for more accurate results across large document sets. The stack runs on Anthropic and OpenAI models, chosen per task.
Every system like this needs evals and observability from day one: tests that check whether agents are doing what they should, and logs that show what happens step by step. Without that, a multi-agent system is a black box, and one subagent's mistake is hard to trace.
Janina, an HR assistant in production since 2026, runs on hybrid RAG across 40 pieces of legislation and more than 900 documents, including Supreme Court case law. Every answer cites the article number, the system tracks ZUS and PIT deadlines, generates HR documents, integrates with KSeF, and pseudonymizes personal data. It runs on WhatsApp.
ClawLabs is the infrastructure layer for hosting AI agents in production: a separate server per agent, EU cloud or on-premise, more than 35 LLM models with automatic failover, and 13 communication channels. It's the layer I use to run multi-agent systems that need high availability and clean separation between agents.
AgriClaw, a digital agronomist in beta since 2026, combines 10-metre satellite imagery, cloud-penetrating radar, and soil moisture data into a single analysis, delivered to the farmer as one sentence on WhatsApp 90 seconds after signup. It's an example of an agentic workflow pulling several independent data sources into one coherent result.
A multi-agent system doesn't run in isolation - I connect it to the client's CRM, ERP, and knowledge bases, with routing across more than 35 language models to match the model to the task and its cost. Clients can bring their own API keys (BYOK) and set hard cost limits instead of an uncontrolled bill at the end of the month.
Data can stay in the EU or move on-premise, in line with GDPR - a decision made together with the client at the rollout stage, not after the fact. Communication with the system can run over WhatsApp, email, phone, an app, or any of the other channels, depending on where people in the business actually work.
The first call runs 60 minutes: you describe what's eating your team's time, I say what can actually be automated - and whether it needs a multi-agent system at all, or a single agent will do. A working prototype, built on the client's real data, follows within 2 weeks; the decision on a full rollout comes only after that.
As a rough market reference, in Poland and the EU in 2026, multi-agent builds with orchestration, RAG, and integrations across several systems tend to fall somewhere between tens of thousands and several hundred thousand PLN, depending on the number of subagents, integrations, and hosting requirements (cloud vs on-premise). These are indicative market ranges, not a fixed price list for this company - every project is quoted after the call and the prototype.
If a process has one step, touches one system, and handles a reasonable volume, a single AI agent is enough - a multi-agent system would just add cost and complexity without real benefit. Orchestration and subagents earn their keep where a process genuinely branches into several independent tasks.
It's also worth planning for ongoing upkeep: monitoring, and updating prompts and tools as the client's systems change. This isn't a ship-it-and-forget project - which is why support after launch continues for as long as it's needed.
We check whether the process genuinely needs multiple agents or a single one will do, and what's actually eating your team's time.
I build working subagents and orchestration on the client's real data, so you see an actual result before committing to a rollout.
The system goes live in the client's cloud, EU cloud, or on-premise, with logging, cost control, and GDPR compliance from day one.
I monitor the multi-agent system and update its subagents for as long as the business needs it.
A single agent handles one task end to end. A multi-agent system splits the process across specialized subagents - each with a narrower scope and its own tools - coordinated by an orchestrator that assigns work and merges results. It fits multi-step processes that touch several systems at once.
Orchestration is the layer that manages the work of multiple agents: it decides which subagent handles which part of a task, in what order, and what to do when one of them returns an error or an incomplete result. Without it, several agents running in parallel quickly start duplicating work or losing context.
A subagent is an AI agent specialized in a narrow slice of a process - for example, only searching documents, or only generating an HR document. Breaking a large task into subagents makes evals and observability easier, since a mistake is far simpler to trace in one module than in a single large agent trying to do everything at once.
It depends on the number of subagents, integrations, and hosting model. As a rough reference, in the Polish and EU market in 2026 such projects tend to fall between tens of thousands and several hundred thousand PLN - these are indicative market ranges, not a price list for this specific company. You get an actual quote after the call and a prototype built on your data.
A working prototype built on the client's real data takes 2 weeks from the first, hour-long call. Full rollout - with integrations, cost controls, and logging - takes longer and depends on how many systems the agents need to connect to.
A multi-agent system makes sense when a process branches into several independent steps and systems - for example HR work that requires searching legislation, tracking deadlines, and generating documents. If the process is a single step touching one system, a single AI agent is enough, and cheaper to maintain.
A single AI agent handles simple, linear processes. A multi-agent system earns its complexity when a process crosses multiple systems. Here is how to tell which one you need.
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Send me two sentences about the problem. I'll reply and tell you straight whether it's worth automating.