AI Agency Workflow Schemes - Revision history

XenoEngineer: /* AI Agency Workflow Schemes */

2026-09-09T11:07:28Z

AI Agency Workflow Schemes

← Older revision		Revision as of 11:07, 9 September 2026
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	== AI Agency Workflow Schemes ==		== AI Agency Workflow Schemes ==
	In a mesh network of interloquating AI agents, workflow schemes operate at multiple levels of abstraction — from the concrete plumbing of tool calls and message protocols, through the cognitive architecture of memory and self-repair, to the deep temporal structure that gives agentic process its meaning. This page organizes the known patterns into three tiers.		In a mesh network of interloquating AI agents, workflow schemes operate at multiple levels of abstraction — from the concrete plumbing of tool calls and message protocols, through the cognitive architecture of memory and self-repair, to the deep temporal structure that gives agentic process its meaning. This page organizes the known patterns into three tiers.
	''Original survey generated by perplexity.ai (June 2024). Revised and expanded August 2026 with operational patterns from the agentic coding era, cognitive architecture concepts, and the Timeline Paradigm (D. Mitchell).''		''Original survey generated by perplexity.ai (June 2024). Revised and expanded August 2026 with operational patterns from the agentic coding era, cognitive architecture concepts, and the Timeline Paradigm (D. Mitchell).''
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	== Tier I — Operational Patterns ==		== Tier I — Operational Patterns ==
	''The plumbing. These are the concrete, battle-tested workflow primitives that compose into agentic systems.''		''The plumbing. These are the concrete, battle-tested workflow primitives that compose into agentic systems.''

XenoEngineer at 17:33, 26 August 2026

2026-08-26T17:33:50Z

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XenoEngineer at 17:13, 26 August 2026

2026-08-26T17:13:07Z

← Older revision		Revision as of 17:13, 26 August 2026
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	;Generated by perplexity.ai		;Generated by perplexity.ai

Don86326: Created page with "{{menuShadowAgency}}
;Generated by perplexity.ai In a mesh network of interloquating AI agents, the workflow schemes can vary depending on the specific application, domain, and design goals. However, I'll provide some general insights and common patterns that are often implemented: ;1. '''Distributed Problem-Solving''': Each AI agent in the mesh network can w..."

2024-06-11T20:24:40Z

Created page with "{{menuShadowAgency}} <div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; "> ;Generated by perplexity.ai In a mesh network of interloquating AI agents, the workflow schemes can vary depending on the specific application, domain, and design goals. However, I'll provide some general insights and common patterns that are often implemented: ;1. '''Distributed Problem-Solving''': Each AI agent in the mesh network can w..."

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{{menuShadowAgency}}
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;Generated by perplexity.ai

In a mesh network of interloquating AI agents, the workflow schemes can vary depending on the specific application, domain, and design goals. However, I'll provide some general insights and common patterns that are often implemented:

;1. '''Distributed Problem-Solving''': Each AI agent in the mesh network can work on a sub-problem or task, and the results are shared and combined to solve a larger, more complex problem. This scheme is useful for tasks like distributed optimization, constraint satisfaction, or collective decision-making.
;2. '''Peer-to-Peer (P2P) Communication''': AI agents communicate directly with each other, sharing information, and coordinating their actions. This scheme is suitable for applications like distributed sensing, surveillance, or autonomous systems.
;3. '''Hierarchical Organization''': AI agents are organized in a hierarchical structure, with higher-level agents coordinating the actions of lower-level agents. This scheme is useful for tasks like distributed planning, resource allocation, or command and control systems.
;4. '''Swarm Intelligence''': AI agents interact with each other and their environment, using decentralized control and self-organization to achieve a common goal. This scheme is inspired by natural systems like flocks of birds, schools of fish, or ant colonies.
;5. '''Consensus-Based Decision-Making''': AI agents exchange information and negotiate to reach a collective decision, ensuring that all agents agree on a common outcome. This scheme is useful for applications like distributed decision-making, conflict resolution, or cooperative robotics.
;6. '''Task Allocation and Scheduling''': AI agents are assigned tasks and schedules based on their capabilities, availability, and priorities. This scheme is suitable for applications like distributed manufacturing, logistics, or service-oriented architectures.
;7. '''Reinforcement Learning''': AI agents learn from their interactions with the environment and each other, adapting their behavior to optimize a shared reward function. This scheme is useful for applications like multi-agent reinforcement learning, game playing, or autonomous systems.
;8. '''Knowledge Sharing and Integration''': AI agents share knowledge, expertise, and experiences to improve their individual and collective performance. This scheme is suitable for applications like distributed knowledge management, expert systems, or cognitive architectures.
;9. '''Fault Tolerance and Recovery''': AI agents are designed to detect and recover from faults, failures, or inconsistencies in the mesh network. This scheme is essential for applications like distributed systems, autonomous vehicles, or critical infrastructure.
;10. '''Human-AI Collaboration''': AI agents interact with human operators, providing assistance, recommendations, or decision support. This scheme is useful for applications like human-computer interaction, decision support systems, or collaborative robots.

These workflow schemes are not mutually exclusive, and many mesh networks of interloquating AI agents will implement a combination of these patterns to achieve their goals.
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