Decision lineage you can replay, not just read
When an autonomous agent takes an action, you need to answer one question fast: why? Lineage links every output back through its tool calls, retrieved context, and prompts — a provenance graph for machine decisions.
- Prompt + context provenance
- Full tool-call trace
- Deterministic replay
- Audit-ready by design
An agent's reasoning is invisible by default
A chatbot that gives a wrong answer is an annoyance. An agent that issues a refund, files a ticket, or edits a record on bad reasoning is a liability — and without lineage, you cannot prove what went wrong.
Most agent stacks emit a stream of opaque events: a prompt went in, some tool calls happened, an action came out. When a stakeholder asks why the agent denied a claim or escalated a case, the honest answer is usually a shrug and a grep through unstructured logs.
Decision lineage closes that gap. We treat every agent decision as a first-class, queryable artifact — a directed graph that links the final action back to the exact prompt, model version, retrieved documents, and intermediate tool results that produced it. The 'why' stops being archaeology and becomes a lookup.
The anatomy of a traced decision
Six layers of provenance, linked into one replayable record per agent run.
Model & version
The exact model, provider, and snapshot ID behind each step — so a behavior change after a model upgrade is attributable, not mysterious.
Retrieved context
Every chunk pulled into the prompt, with source document IDs and relevance scores. Outputs trace to citations, not vibes.
Tool-call trace
The full sequence of tool invocations with arguments, results, and timing — the agent's actual chain of action.
Approvals & overrides
Who approved, rejected, or edited a step, and when. Human-in-the-loop events are part of the lineage, not a side channel.
Branch & threshold
Which risk threshold or routing rule fired, and the values that triggered it — so policy decisions are as traceable as model ones.
Deterministic replay
Re-run a captured decision against the same context to reproduce, diff, or debug it — the difference between forensics and guesswork.
From action to provenance graph
Instrumentation that rides alongside the agent without slowing it down.
Instrument
We wrap the model client, tool layer, and retriever so every call emits a structured span — no scattered print statements.
Correlate
Spans are stitched by a run ID into a single decision graph: output → tool calls → context → prompt → model.
Persist
Records land in your store, redacted and indexed, with retention and access policies you control.
Query
Trace any output to its inputs, replay it, or export an evidence bundle for an auditor — on demand.
Capture everything, store what matters
The naive approach is to persist every byte of every prompt and response forever. That is correct, ruinously expensive, and a compliance hazard. The engineering decision is where to draw the line between completeness and cost.
We capture full fidelity at the boundary, then apply policy: high-risk actions keep verbose lineage indefinitely; routine, low-stakes runs are sampled and summarized. PII is redacted at write time, payloads are compressed, and old records tier to cold storage. You get replayability where it counts without an unbounded storage bill.
- Full-fidelity capture, policy-driven retention
- Redaction and tokenization at write time
- Hot/cold tiering keyed to action risk
Application logs vs. decision lineage
Both record events. Only one lets you answer 'why did the agent do that?'
| Conventional logs | Decision lineage | |
|---|---|---|
| Structure | Flat, timestamped lines | Linked provenance graph per run |
| Answers | What happened, roughly | Why a specific action was taken |
| Replay | Read-only after the fact | Deterministic re-execution |
| Sourcing | Output stands alone | Output → tool calls → source chunks |
| Audit | Manual log spelunking | Exportable evidence bundle |
Frequently asked questions
What exactly does decision lineage capture?
Every input that shaped an action: the model and version, the system prompt, retrieved context with source IDs, the full tool-call sequence with arguments and results, any human approvals, and the final output. Enough to reconstruct why the agent did what it did.
Isn't this just logging with extra steps?
Logs tell you what happened; lineage tells you why. Lineage is a structured, linked graph — output traces back through tool calls to the exact retrieved chunks and prompt that produced it — so you can replay a decision, not just read a timestamped text dump.
How do you keep lineage from leaking sensitive data?
Lineage lives inside your perimeter — your VPC, your object store, your retention policy. We redact or tokenize PII at capture time, scope access by role, and let you set TTLs so records expire on your schedule, not a vendor's.
What does lineage cost in latency and storage?
Capture is asynchronous and off the critical path, so user-facing latency is effectively unchanged. Storage is the real cost; we sample verbose traces, compress payloads, and tier older records to cold storage to keep it bounded.
Related architecture decisions
Lineage is one pillar of a governed agent fleet. Explore the rest.
Make every agent decision answerable
Bring a workflow you're afraid to automate. We'll show you the lineage architecture that makes it auditable enough to trust.