AGENT --- perceive, reason, and act

 

                Agent Types: [simplex reflex, model-based, goal-based, utility, or learning agents]

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• AGENT TYPE: Breakdown of how the different agent types can be enabled 
• by connecting

The Bee Agentic Framework with Equitus.ai's; KGNN and EVS on Power 10 IBM 

servers:

Simplex Reflex Agent: These agents react directly to their environment based on simple rules, without considering past experiences or future consequences.

 

1. KGNN-powered Rule Engine: Utilize (link unavailable)'s KGNN technology to create a 

rule engine that can process simple rules and react to environmental stimuli.

2. Bee Agentic Framework's Sensorimotor Loop: Leverage the framework's

 sensorimotor loop to connect the rule engine to sensors and actuators, enabling the 

agent to react directly to its environment.

Model-Based Agent: These agents maintain a model of their environment and use it to predict future states and make decisions accordingly. 

1. EVS-powered Environment Modeling: Employ (link unavailable)'s EVS technology to 

create a dynamic model of the environment, allowing the agent to predict future states.

2. KGNN-based Planning: Use (link unavailable)'s KGNN technology to generate plans 

based on the environmental model, enabling the agent to make informed decisions.

3. Bee Agentic Framework's Cognitive Loop: Integrate the environmental model and 

planning capabilities with the framework's cognitive loop, enabling the agent to reason 

about its environment.

Goal-Based Agent: These agents are driven by specific goals and choose actions that help them achieve those goals.

1. KGNN-powered Goal Reasoning: Utilize (link unavailable)'s KGNN technology to enable the agent to reason about its goals and generate plans to achieve them.

2. EVS-based Goal Monitoring: Employ (link unavailable)'s EVS technology to monitor the 

agent's progress toward its goals, enabling adjustments to plans as needed.

3. Bee Agentic Framework's Motivational Loop: Integrate the goal reasoning and monitoring 

capabilities with the framework's motivational loop, enabling the agent to prioritize and

 pursue its goals.

Utility Agent: These agents evaluate actions based on their utility (or value) and choose the action that maximizes their overall utility.

1. KGNN-powered Utility Calculation: Utilize (link unavailable)'s KGNN technology to 

calculate the utility of different actions, enabling the agent to choose the most valuable 

option.

2. EVS-based Utility Monitoring: Employ (link unavailable)'s EVS technology to monitor the 

agent's utility over time, enabling adjustments to decision-making as needed.

3. Bee Agentic Framework's Decision-Making Loop: Integrate the utility calculation and 

monitoring capabilities with the framework's decision-making loop, enabling the agent to 

make informed decisions.

Learning Agent: These agents can learn from their experiences and improve their performance over time. 

1. KGNN-powered Learning: Utilize (link unavailable)'s KGNN technology to enable the 

agent to learn from its experiences and improve its performance over time.

2. EVS-based Experience Monitoring: Employ (link unavailable)'s EVS technology to 

monitor the agent's experiences and identify areas for improvement.

3. Bee Agentic Framework's Learning Loop: Integrate the learning and experience 

monitoring capabilities with the framework's learning loop, enabling the agent to adapt and improve its performance.

By connecting the Bee Agentic Framework with Equitus.ai KGNN and EVS on 

Power 10 IBM servers, developers can create a wide range of intelligent agents 

that can perceive, reason, and act in complex environments.