AI agents require to-do lists to stay on track (blog.justcopy.ai)

AI teams at justcopy.ai found that the key to making multi-agent systems reliable in production isn’t a bigger LLM but a stricter architecture: task-driven agents that operate from explicit todo lists with concrete validation gates. Common failure modes—wandering agents that lose focus, amnesiac handoffs between agents, and premature “done” declarations—are solved by a workflow where each todo (id, description, validation criteria, completed flag, evidence) is fetched, executed, and only marked complete after automated evidence-based verification. The pattern includes a centralized todo store, a mandatory loop that pulls the next incomplete task, and a validation gate that prevents phase completion until all todos pass their checks. In practice this eliminated roughly 80% of production failures at justcopy.ai. Technically, the design treats the todo list as external working memory and forces concrete proofs (file checks, HTTP 200 responses, curl outputs) to reduce hallucination and ambiguity. Implementation lessons include milestone-based progress, template-first starts, and specialized agents per responsibility. Operationally, tune agent temperature by phase (infrastructure 0.0, research ~0.4, creative ~0.5), instrument completion rates, token usage, and error frequencies, and require deterministic validation for infra tasks. The result: more debuggable, auditable, and scalable agent workflows that trade unconstrained creativity for predictable, verifiable progress—making AI agents practical for end-to-end production work.