docs: bring pending doc updates onto main

en/ai-sre/agents.mdx

@@ -20,7 +20,7 @@ sidebarTitle: Agent
 
 Delegation doesn't make you wait. After AI SRE delegates a task to a remote agent, the conversation continues immediately. You can keep working or delegate several tasks at once. When the remote agent finishes, its result appears in the conversation as a new message.
 
-Each A2A delegation appears in the conversation stream as a **task card** carrying an `A2A` badge. The card shows the remote agent name, task intent, run status, and usage metrics such as tool call count, tokens, and elapsed time. The run status is one of: initializing, in progress, awaiting input, awaiting authorization, completed, failed (including orphaned), interrupted, or killed. Click the card to view the full delegation trace in the right-hand panel.
+Each A2A delegation appears in the conversation stream as one line: a robot icon, "Dispatched" plus the remote agent name, and the task intent. On the right it shows the run status and usage metrics such as tool call count, tokens, and elapsed time. The run status is one of: initializing, in progress, awaiting input, awaiting authorization, completed, failed (including orphaned), interrupted, or killed. Click the line to view the full delegation trace in the right-hand panel; the `A2A` badge sits in that panel's title, not on the line.
 
 <Tip>
 **The instructions decide whether AI SRE picks this agent.** Before delegating, AI SRE sees only one `name: summary` line per agent and uses it to decide. The summary comes from the `summary:` line at the top of the instructions. The body is delivered in full only on the first delegation, so it can be long and detailed. See [Writing Instructions](#writing-instructions).
@@ -266,22 +266,22 @@ The platform ships several **bootstrap Subagents** that work out of the box:
 | `general` | General-purpose worker with full tool access. Use it to offload work whose intermediate steps would otherwise pollute the parent's context (long reports, multi-file edits, end-to-end builds, repeated probing). Dispatch several in parallel for independent units of work |
 | `explore` | Read-only investigator restricted to `grep` / `glob` / `read`; it cannot write or execute. Returns a compact summary rather than raw search output — ideal for locating evidence inside a codebase or knowledge dump |
 
-**In the conversation**: each dispatch also appears as a **task card** with the same statuses as an A2A delegation. Its badge reads `Agent` instead of `A2A`. The card also shows the **execution environment** of the dispatch (the cloud environment or the BYOC Runner's name).
+**In the conversation**: each dispatch also appears as one line with the same statuses as an A2A delegation: a robot icon, "Dispatched" plus the subagent name (written as `subagent` when the name is a type synonym such as `agent` or `general`), and the task intent. The right side shows the **execution environment** of the dispatch (the cloud environment or the BYOC Runner's name) plus usage and status. Click the line to view the full trace in the right-hand panel; the `Agent` badge (`A2A` for A2A delegations) sits in that panel's title.
 
 **Runtime limits**:
 
 - **Concurrency cap**: a session runs **at most 20 Subagents at the same time**. Total dispatches are unlimited — each completion frees a slot; at the cap, AI SRE waits for running ones to finish before dispatching the rest.
 - **Nesting depth**: a dispatch chain nests **at most 3 levels deep**, preventing a Subagent from spawning itself indefinitely.
 
-**Execution environment**: by default a subtask runs in the same environment as the current session and shares its filesystem, so it can read your workspace's files directly. AI SRE can also send a subtask to a fresh cloud environment (an ephemeral container with its own filesystem; it can reach the public internet but not your private network) or to an online BYOC Runner (account-scope Runners, plus the Runners of the team the session is bound to). If the chosen Runner is unusable, the dispatch fails with the reason and is not re-routed. A2A delegations cannot choose an environment; a remote agent executes in its own. See [Environments (BYOC)](/en/ai-sre/environments) for how the environments differ and how to pick one.
+**Execution environment**: by default a subtask runs in the same environment as the current session and shares its filesystem, so it can read your workspace's files directly. AI SRE can also send a subtask to a fresh cloud environment (an ephemeral container with its own filesystem; it can reach the public internet but not your private network) or to a BYOC Runner (account-scope Runners, plus the Runners of the team the session is bound to). The candidate list includes every Runner that has ever connected, including ones currently offline; Runners that have never connected are not listed. Reachability is checked at dispatch time. If the chosen Runner is unusable, the dispatch fails with the reason and is not re-routed. A2A delegations cannot choose an environment; a remote agent executes in its own. See [Environments (BYOC)](/en/ai-sre/environments) for how the environments differ and how to pick one.
 
 **A cross-environment dispatch cannot read the parent's files**: a subtask sent to another environment gets its own sandbox or Runner directory and cannot see the parent session's files. AI SRE therefore puts everything the subtask needs into its task description, and results come back as text or through an external system such as git or a ticket.
 
 ## Related pages
 
 <CardGroup cols={2}>
   <Card title="Console Chat" icon="comments" href="/en/ai-sre/sessions">
-    Observe A2A delegation task cards and their sub-session panels within a session.
+    Observe A2A delegations and their sub-session panels within a session.
   </Card>
   <Card title="MCP (External Tools)" icon="plug" href="/en/ai-sre/mcp">
     Connect external tools to agents to expand their capability boundary during tasks.