Concepts

DecimalAI is built around a single idea: when your agent changes, you need to know what still holds. Every trace your agent produces is tied to the exact agent configuration that produced it — its manifest. When you change a prompt, swap a model, add a tool, or edit a sub-agent, DecimalAI knows precisely which past traces are still valid and which are now stale. This is manifest-aware versioning, and it is the foundation under everything else. This page is the entry point — each section below has its own focused deep-dive.

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The foundation: manifest-aware versioning

A manifest is a structured snapshot of your agent across ten compatibility surfaces — prompt stack, model runtime, tool registry, skill registry, workflow, sub-agents, output contract, guardrails, context config, and environment. Every trace records the manifest it ran under. When the manifest changes, DecimalAI compares old to new surface-by-surface and tells you, for each past trace, whether it was structurally touched. That single capability is what makes the three product capabilities possible: Each capability works on its own. They compound because they share one model of what an agent is — its manifest.

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The agent lifecycle

Manifest-aware versioning tracks your agent across its whole life — from the first prototype to a retrained model that starts the cycle again. The three capabilities each work standalone, but they compound on one foundation — manifest-aware versioning.

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The data model

These are the core entities DecimalAI captures and how they relate. A session groups the traces of one logical run; each trace is pinned to the manifest it executed under and decomposes into spans and LLM calls; a trace can link to a parent trace when a sub-agent is invoked.

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Read in order

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The improvement loop

Putting all the concepts together, here’s how they connect into a continuous cycle: This loop runs continuously. Each iteration produces a better model, which produces better traces, which produce better training data.

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Where to next