The Geometry of AI Agents — Cycles
With AI agents, cycles are more than repetitive loops. They’re the rhythm, the heartbeat of dynamic systems that drive growth and adaptation. These cyclical processes, much like those in nature, enable the AI to evolve, refine, and interact meaningfully with both the environment and humans.
The power of feedback
At the core of these cycles lies the power of feedback loops. Feedback isn’t just about gathering information—it’s about learning and transforming through that information. Imagine an AI navigating a shifting environment. It evaluates its current state, makes decisions, observes the outcomes, and refines its approach. This process repeats, each iteration building on the last, turning isolated actions into a continuous loop of improvement.
In the diagram above, we have an agent with tools search_docs and search_users. If search_docs has edges connecting to generate and rewrite, it can optionally refine the search query if a discriminator LLM determines the initial docs are not relevant to the query. This cyclical approach enables the agent to rewrite and retry for better results.
Examples in action:
- Decision-making: Evaluate. Act. Learn. Repeat.
- Error correction: Detect mistakes, adjust parameters, and refine outputs.
- Mastery: Write code, debug, and improve—over and over, honing skills with each cycle.
Human-AI collaboration
But cycles aren’t just for machines. They’re bridges between human and AI, creating a dynamic interplay where each complements the other. The iterative refinement cycle is one such bridge: the AI generates initial outputs, humans evaluate, and the AI refines based on feedback. Round after round, the solution becomes more tailored, more sophisticated.
Personalization cycles, for example, allow AI to tune itself to individual preferences over time. This leads to systems that feel less like cold machines and more like companions attuned to your needs.
- Iterative refinement: AI proposes, humans evaluate, AI refines.
- Guided exploration: Humans steer; AI navigates, exploring uncharted territories.
Stability through recurrence
Cycles also enforce stability. They act as checkpoints, keeping systems in check.
- Constraint satisfaction: Encounter a rule violation? Cycle back. Adjust and try again.
- State monitoring: Continuously monitor the system’s state. Reset when needed to maintain equilibrium.
The creative cycle
Creativity, whether human or machine, is not a straight line but a cyclical journey.
- Generate and refine: Create. Assess. Refine. Iterate until it clicks.
- Exploration vs. exploitation: Test uncharted ideas, then focus on promising paths.
This cyclical approach to creativity in AI systems opens up new possibilities for more dynamic and innovative outputs. By incorporating feedback loops and iterative refinement, AI can engage in a form of "digital brainstorming," where initial ideas are generated, evaluated, and then built upon or discarded. This process allows for the emergence of unexpected connections and novel solutions that might be overlooked in more linear approaches.
Moreover, the creative cycle in AI can be enhanced by incorporating external stimuli and cross-domain knowledge. Just as human creatives often find inspiration in diverse sources, AI systems can be designed to cycle through various knowledge domains, combining seemingly unrelated concepts to spark innovative ideas.
In the screenshot above is an example of an Artifact created by Anthropic's Claude during a brainstorming session. This highlights the iterative nature of human + AI collaboration.
Cycles in AI creativity mirror the human creative process: starting with wild exploration and gradually converging toward a polished creation.
Multi-agent harmony
In a world of many AI agents, cycles orchestrate the collective dance.
- Iterative bargaining: Negotiate. Adapt. Balance.
- Synchronization: Align, coordinate, and move as one.
These cyclical interactions turn multi-agent systems into cohesive teams capable of complex problem-solving.
Time and memory
Cycles also give AI a sense of time and history, akin to human experience.
- Memory consolidation: Just like us, AI can consolidate experiences into memory. Review. Distill. Generalize.
- Temporal decision-making: The past informs the present. The present shapes the future.
By incorporating these time and memory-related cycles, AI agents can develop a more nuanced and context-aware understanding of their environment, leading to more sophisticated and human-like decision-making processes.
Multi-scale processing
AI, like life, operates on multiple timescales:
- Immediate reactions for real-time adjustments.
- Mid-term learning for strategic shifts.
- Long-term planning for vision and direction.
Each of these cycles operates in parallel, interweaving insights to create a more robust, adaptive system.
Cycles are the foundation of intelligent systems, infusing AI with:
- Iteration and feedback,
- Adaptation and refinement,
- Continuity and evolution.
This is the geometry of AI agents: not a linear progression, but a cyclical dance of learning, correction, and discovery. Through these cycles, AI moves beyond static responses into a world of perpetual growth and adaptation—always learning, always improving, always in motion.
By embracing these cyclical processes, AI achieves a form of intelligence that mirrors the fluidity and adaptability of human thought. This, ultimately, is the true geometry of AI: not static lines, but dynamic cycles. A perpetual journey of refinement, echoing the very essence of intelligence itself.