Record whether selected fragments led to a successful output. This feeds the reinforcement learning loop: fragments that contribute to successful outputs get boosted in future selections, while unhelpful fragments get suppressed. Args: fragment_ids: Comma-separated fragment IDs success: True if o...
AI agents use record_outcome to create or update resources in Entroly Context Engine — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Entroly Context Engine environment.
This tool writes outcome data into a reinforcement learning system, modifying the weights/rankings of code fragments for future use. It creates or modifies persistent state (RAVS event log, fragment boost/suppress scores) in a reversible way — fragments can be re-scored over time. It does not delete data or execute code directly, making Write the most appropriate category.
From the tool's definition Record whether selected fragments led to a successful output. This feeds the reinforcement learning loop: fragments that contribute to successful outputs get boosted in future selections, while unhelpful fragments get suppressed.
Documented attack patterns abuse exactly the kind of access record_outcome gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Entroly Context Engine, and nothing reaches the server without passing your rules. This is the rule we recommend for record_outcome:
{
"version": "1",
"default": "deny",
"tools": {
"record_outcome": {
"limits": [
{
"counter": "record_outcome_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}record_outcome 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.
Free to start. No card required.
Record whether selected fragments led to a successful output. This feeds the reinforcement learning loop: fragments that contribute to successful outputs get boosted in future selections, while unhelpful fragments get suppressed. Args: fragment_ids: Comma-separated fragment IDs success: True if output was good, False if bad NOTE on RAVS v1: this tool's success flag is also recorded into the RAVS event log as an agent_self_report event with strength=weak and include_in_default_training=False. Default labeling rules ignore it. Use the structured record_test_result / record_command_exit / record_ci_result tools for honest signals you want offline evaluation to actually train against. It is categorised as a Write tool in the Entroly Context Engine MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for record_outcome: 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 Entroly Context Engine. Nothing to install.
record_outcome 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 record_outcome 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 record_outcome. 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.
record_outcome is provided by the Entroly Context Engine MCP server (juyterman1000/entroly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Entroly Context Engine, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
52 Entroly Context Engine tools catalogued and risk-classified — across an index of 43,000+ MCP servers.