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
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
flowchart TD
O[Orchestrator] --> P1[Planner]
P1 --> W1[Worker A]
P1 --> W2[Worker B]
P1 --> W3[Worker C]
W1 --> C[Shared memory]
W2 --> C
W3 --> C
C --> CR[Conflict resolution]
CR -->|consensus| R[Result]
CR -->|disagreement| P1
classDef o fill:#001955,stroke:#00cfff,color:#fff;
classDef w fill:#1414be,stroke:#7fb2ff,color:#fff;
classDef m fill:#283044,stroke:#9fb0d0,color:#fff;
classDef r fill:#3a7d00,stroke:#b9e119,color:#fff;
class O,P1 o;
class W1,W2,W3 w;
class C,CR m;
class R r;
EGITECH
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