Getting high-quality results from AI doesn't come from a single complex command. The key is "harness engineering"—designing structured interaction patterns between specialized agents, such as creating a workflow where an engineer agent hands off work to a separate QA agent for verification.
While AI agents seem tailored for software startups, early traction shows strong interest from traditional industries. A roofing company, for instance, uses agent orchestration to analyze satellite and weather data to generate high-quality sales leads, demonstrating the tool's broad applicability.
AI agents are powerful but amnestic. They need a "heartbeat" checklist—a set of standing instructions—to re-orient themselves on their identity, goals, and tasks every time they activate, just like the protagonist of the film "Memento."
With AI agent orchestration tools, a user's role shifts from a task manager to a board member. Instead of defining granular tasks, you set high-level goals (e.g., MRR targets) and empower a CEO agent to create and execute the plan autonomously.
Running multiple AI agents in parallel quickly leads to "AI sprawl"—losing track of what each agent is doing, what they've accomplished, and how much they're costing. Orchestration tools solve this by centralizing tasks, tracking spend, and providing a unified management dashboard.
Instead of building an AI team from scratch, you can import entire pre-configured company structures from repositories. This paradigm shift, enabled by tools like Paperclip, is akin to acqui-hiring a proven, specialized team for your project with a single command.
AI agents can execute tasks, but they lack inherent values or taste. A founder's primary role evolves into clearly communicating and codifying their unique vision, design sense, and principles for the AI agents to follow, which is the real competitive advantage.
A single AI model is insufficient for running a complex company. An orchestration layer allows you to assign different models (e.g., a powerful frontier model for the CEO, cheaper models for routine tasks) based on their unique "personalities" and cost-effectiveness.