When a Splunk sourcetype is misparsed truncated lines, wrong timestamps, broken line-breaking on multi-line events diagnosing the root cause and authoring a safe fix to props.conf or transforms.conf is specialist, painstaking work. Mistakes at the parsing tier cascade through every search, dashboard, and alert built on that data. Ungoverned automation that deploys changes directly is not an acceptable answer.
The Parsing Remediation Agent runs a fixed five-phase workflow Detect, Diagnose, Propose, Validate, Hand off, where sandbox validation is a required gate before any fix reaches a human." A human approves and deploys. The proposes-never-deploys boundary is enforced structurally by a read-only Splunk MCP server the agent is never on the deploy path, by construction.
A deliberate, high-control design that removes diagnosis toil while keeping humans firmly in control of every deploy.
Runs a fixed five-phase workflow Detect, Diagnose, Propose, Validate, Hand off, where sandbox validation is a required gate before any fix reaches a human."
Backed by a read-only Splunk MCP server exposing 20 of 24 tools in read-only mode. The agent can interrogate the environment but cannot write to it. The deploy path is structurally unavailable.
Every proposed fix is tested in a dedicated Splunk sandbox the agent runs your sample events through the new configuration and checks the outcome before a human reviews it. The validation runs under a hard gate a proposal that fails sandbox validation does not proceed.
The pipeline includes a built-in self-review: before hand-off, every proposal is checked against a fixed rubric schema, safety allowlist, validation completeness so weak proposals are caught before they reach a person.
Designed to draw on prior proposals and reviewer decisions up to 180 days so recurring issues aren't diagnosed from scratch each time. This cross-session memory helps the agent learn from previous decisions and avoid repeating fixes that were rejected.
After validation and self-critique, the agent persists the proposal and hands off a dashboard URL. A human approves, rejects, or requests changes from the dashboard. Deployment always happens outside the agent.
The Parsing Remediation Agent removes the toil of parse-time diagnosis while ensuring no change reaches production without human review.
The proposes-never-deploys boundary is structural. The agent has no path to modify production configuration. It can only read and propose. Human approval is required for every change.
A read-only MCP server and dry-run mode mean the agent is never on the deploy path. This is an architectural guarantee, not a trust-and-verify arrangement.
No proposal reaches the reviewer without passing validation in a version-matched sandbox. Humans spend time approving fixes that are already known to parse correctly not debugging the agent's work.
180 days of prior proposals and human decisions give the agent context. A fix type that was previously rejected is less likely to be proposed again without modification.
The Parsing Remediation Agent compresses the specialist work of parse-time diagnosis into a validated proposal, ready for your approval.
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The Splunk Parsing Remediation Agent is a Claude Code agent built by bitsIO that diagnoses misparsed Splunk sourcetypes, authors validated props.conf and transforms.conf fixes, and submits them for human approval. It does not deploy changes to production humans do.
The agent handles parse-time problems at the props.conf and transforms.conf level including truncated event lines, incorrect timestamp recognition, and broken line-breaking on multi-line events. It scoped to one sourcetype per task deliberately narrow, so every fix is small enough to review with confidence
Mistakes at the parsing tier cascade through every search, dashboard, and alert built on that data. Ungoverned automation that deploys directly creates systemic risk. The agent is designed to remove the diagnostic toil while preserving human control over every change that reaches production.
Every proposed fix is tested in a sandbox environment running the same Splunk version as production. The agent parses sample events through the proposed configuration and validates the outcome before persisting the proposal. A proposal that fails validation does not advance to human review.
After generating a proposed fix, the agent runs an evaluator-optimizer review on its own proposal. This means it attempts to find problems in its own work before a human sees it. Issues caught in self-review are corrected before the proposal is persisted.
The agent is backed by a Splunk MCP server running in read-only mode, exposing 20 of 24 available tools. The read-only restriction is structural the agent can query and inspect the environment but cannot modify it.
The agent hands off a dashboard URL containing the proposal, the validation results, and the self-critique output. A human reviewer approves, rejects, or requests changes from the dashboard. If approved, the human performs the actual deploy the agent is not involved in the deployment step.
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