agentdb_feedback
Record task feedback for learning via LearningSystem + ReasoningBank controllers Use when generic memory_* tools are wrong because you need AgentDB-specific controllers (HNSW vector search, hierarchical tiers, causal-graph links, pattern store/recall, RaBitQ quantization). For simple key-value pe...
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What agentdb_feedback does on Ruflo
AI agents use agentdb_feedback to create or update resources in Ruflo, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ruflo environment.
Why agentdb_feedback is rated Medium
The tool performs a reversible write operation by recording feedback data into a learning system. It creates or modifies records in vector stores and pattern databases rather than executing arbitrary code or deleting data. The severity is medium because misuse could corrupt learning data or inject false feedback patterns that degrade system behavior, though the effects remain reversible through data correction.
From the tool's definition Tool description states 'Record task feedback' which indicates data creation/modification. The tool writes to AgentDB-specific controllers (HNSW vector search, hierarchical tiers, causal-graph links, pattern store) for learning system integration.
Attacks that exploit this kind of access
The rule that runs agentdb_feedback safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ruflo, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For agentdb_feedback, this is the rule to start with:
agentdb_feedback stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Ruflo, apply this rule, and every agentdb_feedback call is checked against it from then on.
Questions about agentdb_feedback
Record task feedback for learning via LearningSystem + ReasoningBank controllers Use when generic memory_* tools are wrong because you need AgentDB-specific controllers (HNSW vector search, hierarchical tiers, causal-graph links, pattern store/recall, RaBitQ quantization). For simple key-value persistence, memory_store/memory_retrieve are simpler. For unrelated file work, native Read/Write are fine. It is categorised as a Write tool in the Ruflo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ruflo MCP server in PolicyLayer and add a rule for agentdb_feedback: 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 Ruflo. Nothing to install.
agentdb_feedback is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the agentdb_feedback 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 agentdb_feedback. 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.
agentdb_feedback is provided by the Ruflo MCP server (ruvnet/ruflo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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