Feedback Loop (Agentic)

You run an experiment, observe results, improve based on what you learned. That's a feedback loop. AI agents need the same thing. The agent takes an action, observes the outcome, and uses that observation to inform the next action. Without feedback loops, agents are reactive and rigid. With them, they become adaptive. Simple feedback: the agent tries an approach, it fails, it tries a different approach. More sophisticated: the agent tracks which approaches work for which problem types, learns patterns, and applies them. The metrics matter. How does the agent measure success? Did the goal get achieved? How efficiently? Were there side effects? Different goals have different metrics. A research agent cares about accuracy and citation quality. A speed-running agent cares about time. A cost-optimizing agent cares about price. The learning speed matters. Can the agent learn within a single task (try approach A, fail, switch to approach B within one problem)? Or does it need to learn across many tasks? Slow learning is useless. Fast learning is powerful. Temporal discounting is interesting too. Should the agent weigh recent feedback more heavily (recent actions are more relevant)? Should it remember all feedback equally? Different contexts answer differently. The exploration-exploitation tradeoff is fundamental. Should the agent try new approaches it hasn't proven work? Or stick with what's working? Too much exploration wastes time on bad approaches. Too much exploitation misses better approaches. The reward signal needs care too. It's easy to accidentally reward the wrong thing. An efficiency-focused agent might learn to cut corners dangerously. An accuracy-focused agent might become overly cautious. Synap's feedback loop framework helps agents observe outcomes, extract learning, and adjust behavior, enabling agents that genuinely improve through experience.