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Using deep-reinforcement learning, model-based planning,

situation awareness & understanding

we create the future of

multi-agent auto-missions

Founded in 2017, AiGENT-TECH develops Train-By-Simulation Man-Unmanned-Teaming (MUM-T) Digital Platform maximazing autonomous systems teaming capabilities with or without humans in the loop.

Train-By-Simulation MUM-T
Digital Platform

Deep Reasoning & Planning AiGENTS 

Our context-aware/deep-reasoning AiGENTs handle real-life complex decision processes under uncertainty, including partial information and noisy observations.

Our planning AiGENTs are capable to construct and execute plans for very complex man-unmanned-teaming missions, using low latency deep-planning algorithms.

Using a novel blend of AI technologies - probabilistic models and inference engines driven by information entropy combined with Bayesian & Deep Reinforcement Learning - our AiGENTs learn faster, with smaller data set, resulting in superior AI Agents with shorter TTM and training time.


Complex Missions
Hierarchical Planning


Low Latency
Deep Planning 



  • Statistical approaches to model human brain function (e.g. the cognitive abilities based on statistical principles).

  • Strong CPUs/GPUs, large memory pools.

  • Knowledge graphs & deep-RL algorithms.

our AiGENTs excel at any task.

Meeting human expert standards, AiGENT-TECH hierarchical planning engine learns and adapts based on experience.


Hierarchical planning, which mimic the human brain decision making process, are designed to function in real world scenarios.


Like humans, hierarchical planning systems should function under uncertainty, ambiguity, partial observation, dynamic environment and sometime even under wrong perception inputs (the most challenging cases).



In order to succeed in their missions, with high probability, these systems should:

  • Actively learn on-line as information changes, and as goals and requirements evolve.

  • Remember previous interactions, in order to increase future success rates (learning based on experience).

  • Resolve ambiguity, tolerate uncertainty and wrong observations. 

  • Interact with the surrounding environment (sensing) and with their end-users so that those users can define their needs. 

  • Assist in 'defining the problem' by asking questions or finding additional input sources if a problem statement is ambiguous or incomplete.

  • Seamless integration with your existing IT platforms

  • Features tailored for your needs

  • 24/7 support 

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Minimal Evidences
Situation Awarness


Situation awareness & understanding is the ability to estimate and predict a possible situation involving multiple actors and/or objects in different locations, in which actors 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 knowledge-graphs, probabilistic modeling, Bayesian inference and model based deep reinforcement learning framework to provide our customers "better than human" situation awareness & understanding solutions. 

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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 

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