> For the complete documentation index, see [llms.txt](https://www.conserver.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.conserver.io/extensions/agent-session.md). # Agent Session **Draft:** [`draft-howe-vcon-agent-session`](https://datatracker.ietf.org/doc/draft-howe-vcon-agent-session/) · **Extension name:** `"agent_session"` ## What it is A growing number of "conversations" involve an AI agent on at least one side. The human-facing surface of that interaction — the words exchanged, the time, the parties — fits cleanly into a normal vCon. The *agent's* internal session — the prompts it received, the tools it called, the responses those tools returned, the files it touched, the reasoning it produced — does not. That data is just as important for audit, debugging, training, and compliance, but it sits in a different shape. The Agent Session extension brings that data into the vCon as a peer to the human dialog. It uses vCon's existing primitives: * The agent is represented as a **party** with `role: "agent"` and structured metadata describing the model. * The session trace lives in **analysis** as a Verifiable Agent Conversations (VAC) document. * Agent-produced artifacts and environment snapshots live in **attachments**. That means agent sessions inherit everything vCon already has — consent, signing, redaction, lifecycle, SCITT. ## When to use it * Documenting LLM-driven contact-center deflection: the LLM's full prompt-and-tool-call trace is auditable alongside the call recording. * Coding-agent sessions (Claude Code, Cursor, etc.) where the conversation, the tool invocations, and the file edits are all part of the same record. * AI-assisted workflows where the agent's output needs to be reproducible: the trace tells you exactly what the agent did and why. * Bridging vCon and [VAC (Verifiable Agent Conversations)](https://datatracker.ietf.org/doc/draft-howe-vcon-agent-session/) — the two specs are designed to compose. ## Spec surface Agent Session adds data in three places. ### 1. Party metadata Each agent participant is a normal party, distinguished by `role: "agent"` and a structured `meta.agent_session` block: ```json { "parties": [ { "name": "Customer", "role": "customer" }, { "name": "Claude", "role": "agent", "meta": { "agent_session": { "model_id": "claude-opus-4-7", "provider": "anthropic", "recording_agent": "claude-code/1.5.0", "environment": "production" } } } ] } ``` ### 2. Session trace in `analysis[]` The full VAC session trace is a JSON document stored as an analysis entry with `type: "agent_trace"`: ```json { "analysis": [ { "type": "agent_trace", "dialog": 0, "vendor": "anthropic", "product": "claude-opus-4-7", "encoding": "json", "schema": "https://datatracker.ietf.org/doc/draft-howe-vcon-agent-session/", "body": "{\"version\":\"1.0\",\"session_trace\":{\"messages\":[...],\"tool_calls\":[...]}}" } ] } ``` The `body` is the VAC document, encoded as a JSON string. `vendor`, `product`, and `schema` identify the agent and the trace format. ### 3. Artifacts in `attachments[]` Files the agent produced, environment snapshots, and tool-call payloads go in `attachments[]` with one of these purposes: | Purpose | Meaning | | ------------------- | ------------------------------------------------------------------------ | | `agent_file_change` | A file the agent created, modified, or deleted | | `agent_artifact` | A standalone artifact the agent produced (e.g. a generated document) | | `agent_environment` | A snapshot of the agent's runtime environment at the time of the session | ```json { "attachments": [ { "purpose": "agent_file_change", "party": 1, "dialog": 0, "filename": "src/payment_handler.py", "mediatype": "text/x-python", "encoding": "none", "body": "def process_payment(amount, ...): ..." } ] } ``` Declare the extension at the top level: ```json { "vcon": "0.4.0", "extensions": ["agent_session"] } ``` ## Relationship to other extensions * **Lawful basis.** Agent-session data is personal data when the agent participated in a conversation with a real person. Use the [Lawful Basis extension](/extensions/lawful-basis.md) the same way you would for the human-only case. * **Lifecycle.** Agent traces benefit especially from a [Lifecycle](/extensions/lifecycle.md) ledger — being able to prove what the agent saw and did, when, is the entire compliance story for AI-assisted workflows. * **WTF.** If the agent session also produced spoken output (TTS), that recording is normal vCon dialog, optionally transcribed via [WTF](/extensions/wtf-transcription.md). ## See also * The `vcon-anthropic-chats` adapter (see [Tools](/tools.md)) converts Claude AI conversation exports into vCons using this extension. * The `vcon-vac` project is the reference implementation tying VAC traces into vCons. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://www.conserver.io/extensions/agent-session.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.