See what you — or another agent in your workspace — actually did over a time window: messages sent, documents created, calls made, plus a summary (run counts, per-day, top tools). Use this to answer 'what did I do today / yesterday / last week / in the last hour?' or 'what did <agent> do?' with r...
Part of the Dialogbrain server.
Free to start. No card required.
AI agents invoke agents_activity to trigger processes or run actions in Dialogbrain. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
agents_activity can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"agents_activity": {
"limits": [
{
"counter": "agents_activity_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Dialogbrain policy for all 157 tools.
These attack patterns abuse exactly the kind of access agents_activity gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
See what you — or another agent in your workspace — actually did over a time window: messages sent, documents created, calls made, plus a summary (run counts, per-day, top tools). Use this to answer 'what did I do today / yesterday / last week / in the last hour?' or 'what did <agent> do?' with real data instead of guessing. Omit agent for your own activity, or pass another workspace agent's name, slug, or id. Pass since/until as ISO datetimes (e.g. '2026-06-03T09:00:00') for sub-day windows like the last hour, or plain dates ('2026-06-03') for whole days — compute them from the current date/time you were given. Defaults to the last 24h. Traces are retained 30 days. Times are interpreted as UTC — if the current time you were given is in another timezone, convert to UTC before passing since/until.. It is categorised as a Execute tool in the Dialogbrain MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_activity: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dialogbrain. Nothing to install.
agents_activity is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the agents_activity rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for agents_activity. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
agents_activity is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 157 Dialogbrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.