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Enabling Robots Think, Act & Team Like Humans Do

Unlike automation (pre-defined synchronization rules between robots), a multi-agent auto-mission is a method to maximize common-mission goals by a group of autonomous robots like people do, including (but not only):

• Semantic situation awareness & understanding

• Decisions making & actions under uncertainty 

• Real-time planning & re-planning

Founded in 2017, we enable complex multi-agent auto-missions with or without humans in the loop.

Our field proven solutions handle physical & virtual multi-agent and multi-action complex missions, taking into account agents common-mission goals. In order to achieve full autonomy and support real-life mission-critical tasks we use a unique combination of model-free deep-reinforcement-learning and model-based planning

Planning & learning algorithms require fast and accurate digital-twin simulatorsOur Trained-By-Simulator and Sim-2-Real-Simulator support acceleration of more than 1000X, enabling cost effective training of multi-agent systems with reduced time-to-market and low-latency real-time planning.

Our deep-reasoning inference engines use various reasoning algorithms (for deduction, induction, abduction and more) and are based on large scale and low latency ontology instances.

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Hierarchical Planning

Our hierarchical planning AiGENTs increase autonomous systems efficiency while operating as a team with or without humans in the loop. Hierarchical planning, which mimics the human brain decision making process, is designed to function in real-life scenarios. Like humans, hierarchical planning AiGENTs should function under uncertainty, ambiguity, partial observations, dynamic environment and sometime even under wrong perception inputs (the most challenging cases). Supporting real-time applications, our deep-planning AiGENTs are capable to construct and execute plans for very complex missions, using low latency deep-planning algorithms.

Last, meeting our customers requirements, we provide an “explainable AI” solution giving high confidence level to human supervisors. 

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Ontology Based Reasoning

Situation awareness & understanding is the ability to estimate and predict a possible situation involving multiple actors and/or objects in different locations, that may trigger events or activities occurring over time, and where the meaning of the situation is revealed by integrating previous knowledge with evidence from multiple sources.

We combine High-order logic, Knowledge-graphs, Stochastic modelling, Bayesian inference and model-based deep reinforcement learning framework to provide our customers "better than human" ontology based deep reasoning AiGENTs. 


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The AiGENT-TECH team has a very rich track record of past successes in developing and deploying complex technology products.

With strong theoretical AI background & proven engineering know-how we create the smartest digital workforce of tomorrow