Skip to main content

Compounding AI Outputs: Building a Memory for Your System

· 4 min read
Pedro Arantes
Pedro Arantes
CTO | Product Developer

In the early stages of AI adoption, most teams treat AI agents as isolated tools: you give a prompt, get a result, and then the context vanishes. This "Task → Prompt → Result → Forget" cycle is inefficient because it fails to capture the intelligence generated during the interaction.

To truly leverage AI in product development, we must shift to a system where outputs are compounded. This means designing workflows where the insight from one agent becomes the context for the next, creating a shared "Memory Layer" that accumulates value over time.

The Problem with Amnesic Agents​

Imagine a scenario where you spend an hour working with an AI to define the architecture of a new feature. You agree on specific constraints, naming conventions, and error handling strategies. The next day, you ask another agent (or even the same one in a new session) to write the code. Without access to the previous decisions, the agent generates code that works but violates all the architectural rules you just established.

This is the "Amnesic Agent" problem. You are forced to manually refactor the code or re-prompt the AI with the same context, wasting time and cognitive bandwidth. This violation of The Principle of Compounding Context leads to rework and frustration.

Building a Shared Memory Layer​

The solution is to treat every AI output not just as a final deliverable, but as a potential input for future tasks. We need to persist these outputs in a way that is accessible to both humans and other agents. This is the essence of Compounding Context.

Here are practical ways to implement this "Memory Layer" in your development workflow:

1. Code as Memory​

Code files are the most durable form of memory for developers. Instead of just pasting the code, use AI to generate comprehensive documentation within the file itself.

  • JSDoc/TSDoc: Have the AI write detailed JSDoc comments explaining why a function exists, its edge cases, and usage examples. This context is visible to future agents reading the file.
  • Architecture Decision Records (ADRs): If an AI helps you make a trade-off decision, ask it to summarize the rationale in a markdown file in the repository.

2. Documentation as a Living Artifact​

User documentation shouldn't be an afterthought. When an AI implements a feature, part of its output should be the update to the user documentation.

  • Immediate Updates: If an agent changes a CLI flag, it should also generate the diff for the README.md or the documentation site.
  • Single Source of Truth: By keeping docs in sync with code, you ensure that future agents (which often read docs to understand the system) have accurate information.

3. Project Management as Context​

Kanban boards and tickets are excellent places to store context for the "Strategy" and "Planning" layers.

  • Ticket Updates: When an AI analyzes a bug, have it post its findings and hypothesis directly to the Jira/Linear ticket. This becomes context for the developer or the next agent who picks up the task.
  • Acceptance Criteria: Use AI to refine vague requirements into strict acceptance criteria and save them to the ticket before any code is written.

4. External Knowledge Bases​

For broader organizational knowledge, use tools like Google Drive, Notion, or internal wikis.

  • Meeting Summaries: AI agents can transcribe and summarize architectural meetings, saving the key decisions into a shared document that can be referenced in future prompts.
  • Brand Voice: Store your product's tone and voice guidelines in a central location that marketing and copy agents can access.

The Corollary of Artifact Persistence​

This approach leads us to a new realization, formalized as The Corollary of Artifact Persistence: AI outputs should be persisted as durable artifacts rather than ephemeral chat logs.

When we treat AI interactions as transient, we lose value. When we treat them as artifact generation steps, we build a compounding asset. Whether it's a comment in code, a line in a markdown file, or a card on a board, every output should find a home in the system's permanent record.

Conclusion​

By moving from isolated tools to a system of compounding context, we transform AI from a simple productivity booster into a "business brain" that learns and grows with the product. Start small: ask your AI to document its own code, update the ticket, or summarize the decision. Over time, these small acts of persistence will build a powerful shared memory that accelerates every future task.