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Action Chains Bridge The Gap Between Agent Vision And Reality

Transform Abstract Agent Concepts Into Precise Technical Requirements

Author

Jason DePerro
January 16, 2025

In partnership with

The Implementation Gap in Agent Design

Building AI agents often feels like attempting to architect a building that constantly shifts its foundation. Traditional design methodologies fall short because they assume linear progression in a fundamentally non-linear space.

Today, I’m introducing a structured approach that brings clarity to this complexity while recognizing the flexible nature of agent interactions.

Understanding Non-Linear Agent Architecture

Traditional UX methodologies rely heavily on predictable user journeys and state transitions. However, AI agents operate in a fundamentally different paradigm that is conversational. Users trigger activities and provide instructions to agents through dynamic interactions that could be text, spoken word, or through imagery from a camera feed.

As an example a content creation agent might simultaneously:

  • Parse reference materials while drafting new content

  • Validate factual accuracy against multiple sources

  • Maintain contextual awareness across document sections

  • Adapt writing style based on real-time feedback

Technical Foundations for Building Agent Action Chains

The Action Chain Framework approaches agent design through systematic decomposition of complex behaviors into atomic interactions. This methodology embraces the inherent non-linearity of agent systems while maintaining architectural coherence.

The Action Chain Framework approaches agent design from first principles. Instead of forcing linear flows, it embraces the dynamic nature of AI interactions through carefully mapped key moments. This framework is akin to a storyboard of a film punctuating the action, characters and plot lines of a scene. These moments in our action chain for our agents are triggered by interactions between a human user and an agent to activate agent capabilities.

Vision Setting: System Architecture (First Page)

The Vision Setting page functions as your system's architectural specification. This critical first step enforces systematic thinking about:

  • System Objectives: Define clear, measurable outcomes

  • Agent Topology: Map the ecosystem of interacting agents

  • User Interaction Model: Specify engagement patterns

  • Problem Domain Constraints: Document technical boundaries

This specification becomes your technical compass, guiding decisions about agent capabilities, interaction protocols, and system constraints.

Key Moments: Interaction Design (Page 2+)

These pages decompose complex agent behaviors into discrete, analyzable interactions. Think of them as architectural diagrams for your agent's cognitive pathways. Each interaction captures:

  • Trigger Conditions: What initiates the interaction

  • Actor Mapping: Human and agent participants

  • Data Requirements: Required inputs and context

  • State Transitions: Expected system state changes

  • Output Specifications: Deliverable requirements

This granular documentation reveals interaction patterns crucial for robust system design.

Deliverables: System Output (Final Page)

The final page bridges theoretical capabilities with implementation requirements through:

  • Output Specifications: Concrete deliverable definitions

  • Quality Metrics: Measurable success criteria

  • Training Requirements: Data quality and quantity needs

  • Evaluation Framework: Performance assessment protocols

Technical Implementation Benefits

The Action Chain Framework provides:

  1. Systematic decomposition of complex agent behaviors

  2. Clear specification of interaction boundaries

  3. Structured approach to quality assurance

  4. Common technical vocabulary for cross-functional teams

Getting Started

Download the Action Chain Workbook template and begin with a focused feature implementation. Complex agent systems emerge from well-architected individual interactions.

How to get your copy

The digital was directly sent to subscribers. Please subscribe for future update and resources. If you want a copy please refer a colleague or friend you think would enjoy fast principles to receive your copy of the Action Chain Workbook.

Join the Technical Discussion

I'm building a community of agent architects and designers. Share your implementation experiences or get technical feedback on your Action Chains.

Questions about implementing the framework? Email me

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🧲 AI Agent Magnet

2025 Future of Jobs Report

Technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition – individually and in combination – are among the major drivers expected to shape and transform the global labour market by 2030.

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The report shows that while AI and technological literacy are among the fastest-growing skills in demand, they need to be paired with distinctly human capabilities like creative thinking, which ranks as the fourth most important core skill. The report also reveals that jobs requiring both technical and creative skills, such as UI/UX Designers, are among the fastest-growing roles.The insight that human-machine collaboration is expected to handle 33% of work tasks by 2030 suggests that creative technologists, who excel at bridging the gap between human needs and technological capabilities, will be increasingly valuable.

Retentive Network: A Successor to Transformer for Large Language Models

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. These properties make RetNet a strong successor to Transformer for large language models. Code will be available at https://aka.ms/retnet.

arxiv.org/abs/2307.08621

This efficiency breakthrough makes it feasible to deploy sophisticated AI agents on mobile devices. Imagine if everyone had a personal, highly aware AI agent onboard their phone or smart glasses?!

Shchmidt makes a point about having "polymaths in people's pockets" suggesting a future where UX design must elegantly blend AI capabilities with human interaction, requiring us to rethink how we design interfaces and experiences. While AI tools are becoming incredibly powerful at tasks like programming and problem-solving, he emphasizes that success will come from thoughtful human-AI collaboration rather than pure automation.

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