Below is a set of commonly discussed agentic patterns and architectural approaches in the field of AI. These patterns often appear in intelligent agent design, multi-agent systems, and autonomous decision-making frameworks. While some patterns originate from theoretical foundations, others stem from well-known implementations and best practices.

1. Reactive (Reflex) Agents

Simple Reflex Agents: Agents that directly map percepts to actions without internal models. They respond immediately based on current input alone (e.g., a thermostat controlling temperature by simple rules).

Model-Based Reflex Agents: Agents that maintain an internal model of the environment to handle partially observable states. They update their model based on incoming perceptions, enabling more nuanced immediate reactions.

2. Goal-Based Agents

Goal-Directed Reasoning: Agents that select actions by considering their goals and how each potential action might help achieve them. Their patterns involve search, planning, and heuristic evaluation to determine which action leads them closer to their objectives.

Constraint-Solving Agents: Agents that represent their environment and objectives as constraints and explore feasible solutions that satisfy these constraints. Their decision-making pattern focuses on reducing the search space to find valid goal states.

3. Utility-Based Agents

Decision-Theoretic Agents: Agents that assign a utility value (or expected utility) to potential outcomes and choose actions that maximize long-term expected utility. This pattern commonly appears in economic modeling and complex decision-making tasks.

MDP/RL-Based Agents (Markov Decision Processes / Reinforcement Learning): Agents that frame their interactions as sequential decision problems, using methods like Q-learning, policy gradients, or value iteration to maximize expected rewards over time.

4. Belief-Desire-Intention (BDI) Agents

Cognitive Architectures (BDI): Agents that maintain explicit representations of their beliefs (information about the world), desires (long-term objectives), and intentions (committed plans of action). These patterns follow philosophical foundations of rational agency, enabling modular reasoning and more naturalistic decision-making.

Layered Reasoning in BDI Systems: Hierarchical patterns that separate high-level abstract reasoning about goals and beliefs from low-level execution details. For example, the PRS (Procedural Reasoning System) framework.

5. Hybrid Architectures

Layered Architectures (e.g., Touring Machines, InteRRaP): Agents that combine reactive and deliberative layers. A reactive layer handles immediate responses while a higher-level reasoning or planning layer manages longer-term strategic goals. This pattern ensures responsiveness while still accommodating complex planning.

Three-Layer Architectures (Reactive, Sequencing, Deliberative): A commonly cited pattern in robotics and AI that has a bottom reactive layer (fast control loops), a middle sequencing or skill management layer, and a top deliberative or planning layer.

6. Multi-Agent Interaction Patterns

Contract Net Protocol: A decentralized pattern where agents announce tasks, and other agents bid for them. The "manager" agent chooses the best bid, exemplifying a market-based mechanism for task allocation.

Coordinator/Facilitator Patterns: A designated agent or set of agents coordinates the activities of others, handling negotiation, resource distribution, or conflict resolution.

Blackboard Architectures: A pattern where multiple specialized agents read and write knowledge to a shared data structure (a "blackboard"). Agents trigger each other’s actions by posting partial solutions or data that others can build upon.

7. Adaptive and Self-Organizing Patterns

Stigmergic Coordination: Agents coordinate indirectly via modifications to a shared environment (e.g., ants depositing pheromones). This pattern emerges in swarm intelligence and behavior-based robotics.

Evolutionary Agent Populations: Patterns where agent behavior is optimized through evolutionary computation or genetic algorithms, ensuring a population of agents adapt over time.

8. Service-Oriented and Ontology-Based Agent Patterns

Agent-Oriented Software Engineering (AOSE) Frameworks: Patterns that follow methodologies (e.g., Gaia, Tropos) to define roles, interactions, and organizational rules that shape agent behavior.

Ontology-Driven Agents: Agents that rely on shared semantic vocabularies for communication and coordination, enabling interoperability and modular composition of knowledge bases.

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These patterns offer a conceptual toolkit for designing, analyzing, and implementing intelligent agents and multi-agent systems. Designers often combine multiple patterns—for instance, layering a BDI reasoning process atop a reactive framework or incorporating utility-based decision-making into a multi-agent negotiation protocol—to address complex, dynamic environments.