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agents_simulate_inbound

Replay an inbound message on a thread through the real trigger pipeline and return what would have happened. The router auto-picks the winning enabled agent + trigger by priority/specificity (same logic as production). By default send_mode='draft' so no real message is sent; pass send_mode='auto'...

Part of the Dialogbrain server.

agents_simulate_inbound can trigger actions in Dialogbrain, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents invoke agents_simulate_inbound 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_simulate_inbound 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.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "agents_simulate_inbound": {
      "limits": [
        {
          "counter": "agents_simulate_inbound_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

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These attack patterns abuse exactly the kind of access agents_simulate_inbound gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so agents_simulate_inbound only ever does what you allow.

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Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the agents_simulate_inbound tool do? +

Replay an inbound message on a thread through the real trigger pipeline and return what would have happened. The router auto-picks the winning enabled agent + trigger by priority/specificity (same logic as production). By default send_mode='draft' so no real message is sent; pass send_mode='auto' on a test account to let the matched agent actually deliver (drafts get overwritten by the next draft, so 'auto' is the only way to verify Telegram/email delivery end-to-end). Use to verify routing for a thread: which agent answers, which trigger wins, or — when nothing matches — the structured skip reason. Pass blockchain_tx_data instead of message_text to simulate a blockchain:transfer event on the thread. Returns: {matched: true, matched_agent: {id, name, execution_mode}, matched_trigger: {id, trigger_type, conditions, specificity_score}, routing_reason, response_text, messages[], execution_mode, send_mode, model_used, tokens_input, tokens_output, latency_ms, rag_queries_made, rag_results_used} on a hit, or {matched: false, skip_reason, simulator_warnings} on a miss.. 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.

How do I enforce a policy on agents_simulate_inbound? +

Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_simulate_inbound: 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.

What risk level is agents_simulate_inbound? +

agents_simulate_inbound is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit agents_simulate_inbound? +

Yes. Add a rate_limit block to the agents_simulate_inbound 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.

How do I block agents_simulate_inbound completely? +

Set action: deny in the PolicyLayer policy for agents_simulate_inbound. 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.

What MCP server provides agents_simulate_inbound? +

agents_simulate_inbound 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.

Enforce policy on every Dialogbrain tool call.

Deterministic rules across all 157 Dialogbrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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