What The Context Company captures

For every production interaction, The Context Company records the complete execution alongside the transcript. That data is what powers patterns, failure detection, Insight Search, Recaps, and MCP.

Captured for every run

Data	Captured	Notes
User prompt	Yes	The initial input to the agent.
Agent response	Yes	The final output returned to the user.
Multi-turn conversations	Yes	Grouped as a session when you set `tcc.sessionId`.
Runs	Yes	One end-to-end agent execution. See runs.
Model calls (steps)	Yes	Each LLM request and response inside a run. See steps.
Tool calls	Yes	Every tool invocation triggered by the model.
Tool arguments	Yes	The full arguments passed to each tool.
Tool results	Yes	The result returned by each tool.
Errors	Yes	Exceptions, error classes, and status messages at the run, step, and tool-call level.
Latency	Yes	Duration per run, step, and tool call.
Token usage	Yes	Prompt and completion tokens per model call.
Model cost	Yes	Cost per model call, aggregated per run.
Sessions	Yes	Set `tcc.sessionId` to group runs into a conversation.
Conversational marker	Yes	Set `tcc.conversational` to enable behavioral analysis on real-user runs.
Agent identifier	Yes	Set `tcc.agent` to promote runs to a first-class agent.
User identity	Yes	Set `tcc.userId` (and optionally `tcc.userName`) to segment by end user.
Organization identity	Yes	Set `tcc.orgId` (and optionally `tcc.orgName`) to segment by account or tenant.
User feedback	Yes	Thumbs up / thumbs down and up to 2,000 characters of free text per run.
Custom metadata	Yes	Arbitrary key/value fields attached to a run for filtering and analysis.

Why execution-level data matters

Conversation-only analytics can only see what was said. Execution-aware analytics can see what actually happened. That difference matters because agents can produce plausible-looking transcripts on top of broken executions. Concrete examples that execution data makes visible:

A support agent tells the user “your refund has been processed” but the issueRefund tool returned an error the model ignored.
A retrieval agent generates a confident answer even though the retrieval tool returned an empty result.
An agent retries the same tool with the same arguments five times in a loop, burning tokens without progress.
A tool call succeeds technically (200 OK) but writes the wrong record to a downstream system.
The agent calls the wrong tool for the user’s intent and produces a fluent but incorrect answer.

Because tool arguments, results, and errors are stored per run, these failures show up in patterns, the failures view, and Insight Search even when the transcript looks fine and the user never complained.

The data hierarchy

Everything The Context Company captures rolls up through the same structure:

Organization (your customer / tenant)
  └── User (end user of your agent)
       └── Session (conversation, if grouped)
            └── Run (one end-to-end agent execution)
                 ├── Model step (LLM request + response)
                 │    └── Tool call (name, arguments, result, error, latency)
                 ├── Feedback (thumbs, text)
                 └── Metadata (custom + reserved tcc.*)

Read the Concepts page for the full definition of each level and the exact metadata keys used to populate them.

How to attach identity and context

Reserved tcc.* metadata keys promote fields into first-class dimensions:

tcc.sessionId, tcc.conversational, tcc.agent
tcc.userId, tcc.userName
tcc.orgId, tcc.orgName

Everything else you attach becomes custom metadata that can be filtered in the dashboard, API, and MCP. Framework-specific syntax is documented on each integration page.

Concepts

Runs, steps, tool calls, sessions, users, and organizations.

Traces

How captured data renders as a full trace.

Data collection

What is stored, where, and for how long.

PII redaction

Redact sensitive fields at ingest.

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What The Context Company captures

Captured for every run

Why execution-level data matters

The data hierarchy

How to attach identity and context

Concepts

Traces

Data collection

PII redaction

​Captured for every run

​Why execution-level data matters

​The data hierarchy

​How to attach identity and context

​Related

Concepts

Traces

Data collection

PII redaction

Captured for every run

Why execution-level data matters

The data hierarchy

How to attach identity and context

Related