Trigger agent adaptation based on feedback Use when native Task is wrong because you need agents that adapt their cognitive pattern (convergent / divergent / lateral / systems / critical) per-task and share knowledge across the swarm. For static one-shot agents, native Task is fine.
AI agents invoke daa_agent_adapt to trigger actions in Claude Flow. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes external operations (agent adaptation and knowledge-sharing across a swarm) whose effects depend on feedback arguments. While it does not directly delete data or move money, it modifies the operational behavior of an agent fleet in ways that could have unintended consequences if misapplied by an AI—for example, adapting agents to harmful cognitive patterns or triggering uncontrolled swarm behavior.
From the tool's definition Trigger agent adaptation—the tool initiates dynamic modifications to agent behavior patterns. The description indicates it causes agents to 'adapt their cognitive pattern' and 'share knowledge across the swarm,' which are runtime behavioral changes triggered…
Attacks that exploit this kind of access
Trigger agent adaptation based on feedback Use when native Task is wrong because you need agents that adapt their cognitive pattern (convergent / divergent / lateral / systems / critical) per-task and share knowledge across the swarm. For static one-shot agents, native Task is fine. It is categorised as a Execute tool in the Claude Flow MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Claude Flow MCP server in PolicyLayer and add a rule for daa_agent_adapt: 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 Claude Flow. Nothing to install.
daa_agent_adapt 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 daa_agent_adapt 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 daa_agent_adapt. 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.
daa_agent_adapt is provided by the Claude Flow MCP server (claude-flow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.