Architecture and orchestration
Choosing the interaction pattern for the task: supervisor, pipeline, free cooperation. The cost, latency and predictability of the system depend on it.
Multi-agent architectures · LLM · Autonomy
Multi-agent systems: when one agent is no longer enough
A multi-agent system is not “a lot of bots”. It is an architecture in which specialised agents split up a task, use tools, remember context and work under control. The difficulty lies not in the prompts but in orchestration, observability and the limits of autonomy.
For teams that have hit the ceiling of a single agent: the task needs several roles, a long context, calls to external systems and predictable behaviour under load.
What is included
Tools and integrations
Agents that can call your systems: databases, APIs, search. With failure handling — because a tool will return an error one day.
Memory and knowledge
Knowledge graphs and vector search as long-term memory: so that the system remembers context between sessions and relies on your data, not on the model’s guesses.
Quality evaluation
An evaluation system before production: test sets, metrics, regressions. Without it, improving a prompt is guesswork, not engineering.
Control of autonomy
Limits of authority, human confirmation points, an audit of actions. Autonomy is useful exactly as long as it is observable and reversible.
How we work
Task analysis
We decide whether a multi-agent design is needed at all: one agent with good tools is often enough, and that is cheaper.
Prototype and evaluation
We build the system and, right away, the loop that evaluates it. The metric appears before the optimisation.
Production
Observability, the cost of calls, degradation under load, failure handling.
Operation
Quality monitoring, further training, development. Model behaviour changes — the system has to be watched.
Why us
We live in these systems
Our engineers work alongside teams of agents every day. We do not only build such systems — we use them, and we know where they break.
Our own developments
We have our own work in agent orchestration and knowledge graphs, not just experience assembling other people’s frameworks.
Engineering instead of prompt magic
Quality evaluation, versioning, regression tests. We treat agents as software that has to live in production.
Cases
AI infrastructure / multi-agent systems
In-house productAgent teams assembled by configuration that review their own work
Core of a multi-agent platform built in 2.5 weeks: 29 declarative agents in three teams, agent-critic pairs, shared memory on four databases, five autonomy levels.
- 25,000+
- lines of Python in the platform core across 134 modules, counted from the repository
- 29
- declarative agents in three ready-made teams — engineering, cognitive, research; counted from YAML specs
AI infrastructure / multi-agent systems R&D
Research projectA multi-agent environment where rules are code, not prompts
In four days we built and open-sourced (MIT) an agent environment where constraints are enforced in code: cryptography, Byzantine consensus, reproducible experiments.
- same hash
- a repeated run and a replay of the log give a byte-identical final event hash — anyone can repeat it from the MIT-licensed code
- 187 of 187
- engine tests green — master branch, commit 625b81d, as of 05.10.2026
Frequently asked questions
What is a multi-agent system, in simple terms?
It is a system in which a task is solved not by one program but by several autonomous participants — agents — each with its own role and its own tools, exchanging results. The analogy is not one all-purpose employee but a team of specialists with divided responsibilities.
When do you need a multi-agent system, and when is one agent enough?
One agent copes as long as the task fits within its context and a single set of tools. A multi-agent design is justified when you need different roles with different permissions and knowledge, when steps can run in parallel, or when it matters that one participant checks another’s work. Without that, a multi-agent design adds cost and points of failure with no gain.
How predictable are such systems?
Exactly as predictable as the control loop built around them: quality evaluation, limits of authority, human confirmation points and an audit of actions. Language models are probabilistic, so predictability comes from the architecture around them, not from hoping the model behaves.
Can it run on models inside a closed perimeter?
Yes. We deploy solutions on local models when data cannot leave the perimeter.
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