Topic:
AI Architecture
Here's the hard truth - an AI agent without a structured understanding of time is just a text generator flailing in the dark. I've found that the most effective way to orchestrate agents is to map your documentation directly to cognitive functions. You need to give the agent a clear framework for reasoning about the project's state.
Let's break it down into the four pillars of this cognitive architecture.
First, you have the Past. This is your 'changelog.md'. It's not just a list of commits; it's the history of the project's evolution. It tells the agent where we've been and what decisions led us here.
Second is the Present. This is your 'architecture.md'. This file needs to be an evergreen representation of your current reality. In my workflows, I instruct the agent to analyze the existing code and update this document constantly. It is the absolute truth of 'now'.
Third is the Future. This is where most people get it wrong. Your 'requirements.md' shouldn't be a static list of MVP features. It needs to be a living snapshot of the future state - a vision of what the system looks like when the work is done.
Finally, you have the Plan, or 'roadmap.md'. This connects the present to the future. It's the bridge. When you change the requirements, it must trickle down to the roadmap. This structure gives the agent a complete mental model - it knows what happened, what exists, where we're going, and how to get there.
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Implementing this structure requires a radical shift in how you work. You stop being a coder and start being an architect of intent. Instead of writing code, you manage the state of these documents.
Here's what the workflow looks like in practice. When I want to change the system, I don't touch the code. I edit the 'requirements.md' to reflect the new desired future state. Because the agent understands the relationship between these files - governed by a system prompt like 'claude.md' - it automatically identifies the gap between the 'architecture.md' (present) and the 'requirements.md' (future).
The agent then updates the 'roadmap.md' to bridge that gap. It creates a plan. This is high signal work. You aren't micromanaging lines of code; you are orchestrating the project's evolution.
This approach reframes prompt engineering entirely. We aren't just writing clever prompts anymore. We are building a cognitive architecture for agents. We are defining their understanding of time and purpose. This provides a much more sophisticated interaction model than the standard 'read this file and fix this bug' approach.
If you want ownership over complex AI systems, you have to treat documentation as the interface for reasoning. The files are the brain. You are simply guiding the thoughts.
Ready to stop writing code and start orchestrating intelligence? At Ability.ai, we build agents that understand your business logic as deeply as you do. We help you move from static files to dynamic cognitive architectures. Let's talk about how to deploy agents that actually understand the past, present, and future of your software.