Research / Lab

We study how AI agents collaborate — and turn it into systems we ship.

Single models are powerful, but real work happens when agents coordinate. We focus on the hard parts, and measure whether the whole system actually gets better.

Focus areas

What we study

Collaboration & coordination

How agents plan together, delegate, and share memory without stepping on each other.

Conflict & consensus

When agents disagree: voting, critique, or escalation to a human.

Evaluating multi-agent systems

Measuring emergent behavior, not just single-prompt accuracy.

Agentic retrieval

Search that reasons, queries many sources, and verifies — beyond static RAG.

A collaboration pattern

Orchestrator, workers, shared memory, conflict resolution

Workers run in parallel and write to shared memory. Disagreement is resolved explicitly, or sent back to the planner, before a result is produced.

A pattern, not a product diagram: the shape we use when several agents must agree.

Read our thinking →

Notes on AI engineering and multi-agent delivery.

See the pattern demo →

A planner and a synthesizer agent working through a request. Live when available, otherwise a labelled sample run.

EGITECH

Have an AI idea to build, or a legacy system to modernize? Let's scope it with our multi-agent team.

Get Free Consultation