This release brings four AI SRE changes: one Customize area for settings, memory you can see and edit, subagents that run in other environments, and Monitors data sources in AI SRE.
Settings in one Customize area
Knowledge, memory, plugins, and environments used to sit under separate menus. They now all live under AI SRE → Customize, with a second-level sidebar in three groups:
- Context: Knowledge, Memory
- Plugins: Overview, Apps, Skill, MCP, Agents
- Environments: Self-hosted, Cloud
Each Plugins and Environments item shows how many entries are configured. Row actions in the Skill, MCP, Agents, and self-hosted environment tables move into a "…" menu at the end of the row. The plugin overview adds search, lists enabled entries first, and marks entries that need your authorization.
Memory you can view and edit
Memory is the preferences, procedures, facts, and lessons AI SRE keeps from conversations. Each memory is now a Markdown file, and you can view and edit it at Customize → Context → Memory.
- Two sources. You ask in a conversation to remember, change, or forget something, or the system writes down what later sessions will need when a session ends.
- Two scopes. A session bound to a team writes to that team's memory. A session without a team writes to your personal memory. Each new session starts by reading the memory in its scope.
- Preferences win. A preference you stated, such as "lead with the conclusion" or "use this time zone", takes precedence over a Skill's or tool's default when the two conflict.
- Edit with AI. The input box at the bottom of the page can change several files at once. AI SRE shows a plan first and writes nothing until you confirm.
- No silent overwrites. Each save carries the file's version. If someone else or another session changed the file in the meantime, the save is rejected and your draft is kept so you can review and submit again.
Subagents can run in another execution environment
When AI SRE dispatches a subagent, it can choose where the subagent runs: the cloud, or a self-hosted runner you are allowed to use. Before dispatching, the parent session can see which MCP connectors each environment has and what they do, and send the task to the environment that has the connector.
For example, the parent session runs in the cloud, and the MCP connector for a database is bound only to a runner inside your network. The parent can dispatch a subagent to that runner, let it query through the connector, and get the result back.
A subagent in the same environment shares the parent's workspace. A subagent in another environment gets its own session and workspace. In the conversation, each subagent row shows the environment it actually ran in, its token usage, and its duration. Environment permission checks and tool approval rules are unchanged.
Monitors data sources in AI SRE
From the Monitors → Data sources list, you can hand a data source straight to AI SRE:
- Ask in AI SRE → in the name column opens an AI SRE conversation with a question that names the data source and its type already filled in. You decide when to send it.
- The five diagnosis-only types, Redis Node, Redis Sentinel, MongoDB (mongod / mongos), and Kafka, get an AI analysis button on the row. It opens the AI SRE panel on the current page with the data source as context, and AI SRE connects to it once with its diagnosis tools. These types have no query workbench, so this is also how you confirm they connect.
These entries appear only when the data source is enabled, the account has AI SRE turned on, and you have AI SRE chat permission.
Get started
Go to Flashduty console → AI SRE → Customize.
Product docs: Memory, Context, Agents and subagents, Monitors data sources.