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:| Capability | What it answers | What manifest-awareness gives it |
|---|---|---|
| Regression checks | Did this change break anything? | Knows exactly which surfaces changed, so it scopes the check to traces that actually depend on them |
| Skills registry | Which skills measurably help? | A skill is a manifest surface; SkillScore is measured against the manifest the trace ran under |
| Training-data validation | Which traces are safe to train on? | Stale traces — those whose manifest no longer matches the current agent — are filtered before they poison a dataset |
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.| Stage | What happens | What manifest-awareness does |
|---|---|---|
| Prototype | You build the agent and capture first traces | Records the initial manifest as the baseline |
| Ship | The agent runs in production | Stamps every trace with the manifest it ran under |
| Improve | You change a prompt, model, tool, or sub-agent | Runs a regression check; flags which past traces are now stale |
| Retrain | Valid traces become a training set; you fine-tune | Filters out stale traces, then a better model ships and the loop repeats |
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.| Entity | What it is |
|---|---|
| Session | A logical run that groups one or more traces |
| Trace | One agent execution, pinned to the manifest that produced it |
| Manifest | The structured snapshot of the agent across its ten compatibility surfaces |
| Span | A single step inside a trace (a tool call, a retrieval, a sub-step) |
| LLM Call | A single model invocation recorded within a trace |
| parent_trace_id | Self-link on Trace — set when a sub-agent trace is spawned by a parent trace |
Read in order
Execution Model
Traces, spans, LLM calls, sessions — how DecimalAI captures and organizes agent activity.
Versioning & Compatibility
Manifests, components, compatibility verdicts, repair — the core innovation.
Evaluation
Evaluators, eval scores, eval verdicts, the unified decision engine.
Multi-Agent Systems
Orchestrators, sub-agents, delegation vs handoff, drift detection.
Skills & Data Pipeline
Skills (SKILL.md), datasets, export formats (SFT/DPO), replay, repair.
Glossary
Quick A-Z reference for any term.
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.Where to next
Quickstart
If you’re new — install the SDK and get your first trace in 5 minutes.
2-Minute Demo
One command seeds a demo agent and links you to a live impact report.
Glossary
A-Z quick reference for any term used in these concept pages.
API Reference
Every REST endpoint with examples and schemas.