Shared context.
A common working memory connects resource signals, asset relationships, constraints, and previous evaluations. Agents reason from a consistent mission context.
THE MULTI-AGENT INTELLIGENCE PLATFORM
One mission.
Many agents.
Shared intelligence.
A self-learning core. A network of specialist agents. Intelligence designed to work as one.
We’re building an AI platform that coordinates RL agents across discovery, reasoning, planning, and evaluation—with shared context and feedback at every step.
ORCHESTRATION / SPECIALIST POLICIES / SHARED LEARNING
SENTIENT / MISSION CONTROLINTERACTIVE PREVIEW
SCOUT
MATCH
SENTIENT CORECoordinate · reason · learn
PLAN
EVALUATE
READY / 4 SPECIALIST AGENTS
See how a mission moves through the proposed agent network.
Scripted product preview. No live agents or external actions.
THE AGENT LOOP01 Observe→02 Reason→03 Coordinate→04 Evaluate→05 Learn
01 / MISSION DOMAIN: ENERGY + COMPUTE
Energy + Compute Radar.
Our first mission domain: connecting energy resources to compute infrastructure. Explore the candidate opportunities specialist agents are designed to investigate.
RESOURCE TERRAIN / SCHEMATIC
NOT A GEOGRAPHIC SITE SURVEY
NOT A GEOGRAPHIC SITE SURVEY
+ RESOURCE SIGNAL - - POTENTIAL CONNECTIONN ↑
DEMONSTRATION ONLY — capacities and distances are fictional scenarios. Availability, ownership, permits, and interconnection require verification.
02 / THE AGENT NETWORK
One learning core.
Specialist agents at every step.
MULTI-AGENT PLATFORM / IN DEVELOPMENT
Our architecture connects a central learning agent to specialist reinforcement-learning agents across scouting, asset matching, configuration planning, and evaluation. Like a brain coordinating specialized functions, the core shares context and uses feedback to improve decisions across the system.
PROPOSED ARCHITECTURE / SHARED CONTEXT + FEEDBACK
Central self-learning agent
Coordinates goals, specialist policies, and shared learning
Resource scouting
RL agentAsset matching
RL agentSystem planning
RL agentEvaluation
RL agent
RL agentAsset matching
RL agentSystem planning
RL agentEvaluation
RL agent
↻ Simulation + outcomes + expert review → rewards → updated policies
A connected intelligence.
A continuous learning loop.
Resource agents identify potential. Asset agents find connections. Planning agents assemble energy-and-compute systems. Evaluation agents return evidence to the coordinating core.
- 01Scout & connect
Specialist agents link resource signals to power, grid, land, fiber, and compute assets.
- 02Coordinate & evaluate
The central agent coordinates planning and evaluation agents to compare configurations against shared objectives.
- 03Learn & feed back
Simulation, measured outcomes, and expert review provide reward signals to improve agent policies and future proposals.
COMBINATION EXPLORERINTERACTIVE CONCEPT
This explorer displays predefined scenarios. It does not run a trained RL policy, scout live resources, or validate engineering feasibility.
03 / THE SHARED LEARNING LOOP
Intelligence compounds
through feedback.
The proposed learning loop connects specialist decisions to shared objectives. Each reviewed outcome becomes evidence for the next training cycle.
Coordinated policies.
The core decomposes a mission, routes work to specialist agents, and reconciles their proposals against shared goals and constraints.
Learning with evidence.
Simulation and reviewed outcomes generate reward signals. Policy updates are evaluated before use; external actions remain subject to operator approval.
LET’S BUILD WHAT COMES NEXT
Bring a mission.
Build with our agents.
We’re looking for design partners with real resource-planning challenges, domain expertise, and evaluation data. Help shape the first missions of Sentient Systems.