An agent needs a runtime. That runtime is typically an event loop. The loop: (1) agent observes current state, (2) decides what to do, (3) executes the decision, (4) observes the result, (5) updates state, (6) repeat until done. Each cycle is called a 'step' or 'tick.' The challenge is designing event loops that are efficient and reliable. Simple event loops are synchronous (step, wait for completion, next step). But steps might call APIs that take time. You're blocking waiting. Asynchronous event loops handle multiple steps concurrently. Agent A calls an API, doesn't block. Agent B continues working. When Agent A's API returns, its event loop is notified and resumes. The state management is critical. An agent's state includes its observations, its goals, its memory, its current task. As the loop executes, state evolves. If state gets corrupted or lost, the agent gets confused. Logging and recovery are important. If the agent crashes mid-step, you need to recover its state and resume. Determinism is interesting. Should an agent's behavior be deterministic? If you run the same agent again with the same inputs, should you get the same result? Sometimes you want determinism (debugging, reproducibility). Sometimes you want randomness (exploration, avoiding stuck loops). The event loop architecture affects this. The timeout problem is real. Some steps take longer than others. If a step takes 30 minutes, should the loop wait? Should it timeout and try another approach? Different applications answer differently. Synap's event loop runtime handles agent lifecycle, state management, concurrency, and recovery, letting developers focus on agent logic rather than runtime plumbing.
Why It Matters
Event loops are invisible when they work and catastrophic when they don't. Poor event loop design causes agents to crash mysteriously, lose state, behave unpredictably. Good event loop design lets agents run reliably and efficiently. It's foundational infrastructure.
Example
An agent's event loop: (1) observe current state (task, context, observations), (2) LLM decides next action, (3) execute action (call API, run code, whatever), (4) capture result, (5) update state, (6) check if done, if not loop to step 1. This simple loop, repeated thousands of times, is how agents reason through problems.