Building Self-Adaptive AI Agents with Google Gemini & SAGE Framework

A recent technical implementation demonstrates the construction of an advanced AI agent system designed for self-adaptive, goal-oriented execution. This system leverages Google’s Gemini API within a structured framework known as SAGE, an acronym for Self-Adaptive Goal-oriented Execution. The objective is to develop an intelligent agent capable of deconstructing complex high-level goals, formulating strategic plans, executing tasks systematically, and continuously learning from its outcomes to improve performance.

The SAGE framework is built around four core components, each playing a crucial role in the agent’s autonomous operation:

1. Self-Assessment (S): The agent begins by evaluating its current state and capabilities relative to the overarching goal. This involves a comprehensive analysis of its progress, available resources, identified knowledge gaps, potential risks, and initial recommendations for next steps. This assessment provides a foundational understanding for subsequent planning.

2. Adaptive Planning (A): Following self-assessment, the agent dynamically generates a series of actionable tasks. This planning phase is context-aware, meaning it considers the current state and assessment findings to create a flexible and prioritized task list. Tasks are defined with specific descriptions, priority levels, and dependencies, ensuring a structured approach to goal achievement.

3. Goal-oriented Execution (G): With a plan in place, the agent proceeds to execute individual tasks. Each task is approached with focused attention, broken down into concrete actions, and performed methodically. The execution phase emphasizes validation at each step to ensure results are accurate and contribute effectively towards the overall goal.

4. Experience Integration (E): After a task is executed, the agent integrates the outcome into its knowledge base. This crucial learning phase involves analyzing the results—whether successful or failed—to extract key insights, identify recurring patterns, and determine necessary adjustments for future actions. This continuous learning cycle allows the agent to refine its strategy and improve its decision-making capabilities over time.

At the technical core, the system is implemented using Python, leveraging the Google Gemini API for its generative AI capabilities. Fundamental components include a Task data structure to manage individual units of work, tracking their status (pending, in progress, completed, or failed), descriptions, priorities, and dependencies. The SAGEAgent class orchestrates the entire cycle, managing the agent’s memory, tasks, and contextual information across iterations.

The agent operates in an iterative loop. In each cycle, it performs a self-assessment, generates a new set of tasks based on the assessment, executes the most relevant tasks, and then integrates the learned experience. This allows the agent to dynamically adapt its approach as it progresses, addressing new challenges or refining existing strategies based on real-time feedback.

For demonstration, the framework was applied to a goal such as “Research and create a comprehensive guide on sustainable urban gardening practices.” Through multiple SAGE cycles, the agent would assess its understanding, plan research tasks, execute them by generating content, and learn from the quality and relevance of its output. This iterative process highlights how the system assesses its progress, generates actionable tasks, executes them, and refines its strategy through learned experience.

This modular design offers significant potential for extension, enabling the development of more complex, multi-agent environments or highly specialized applications tailored to specific domains, demonstrating a step towards more autonomous and intelligent AI systems.