Autonomous Remediation for AI Agents

Autonomous remediation is a runtime capability that executes a pre-approved, scoped response against an AI agent the instant a policy violation is observed, with no human in the closure loop. The action is bounded to one blast-radius unit (one agent, one token, one tool call, one connector), reversible where possible, and logged as evidence. That is the whole promise of autonomous remediation: response that lands in the same second the agent acts.

This is not a SOAR playbook firing on a SIEM alert. Those workflows assume the incident is minutes or hours old. Agents do not give you minutes. They move roughly 16 times more data than human users and complete action chains before the first notification lands. Obsidian does not sit inline as a proxy. It hooks into the AI platform through native APIs and an "Intel Inside" decision-engine model, so the enforcement is native to the platform rather than a network chokepoint.

One honest boundary up front. Autonomous runtime block is live for Claude and Microsoft Copilot only today. For Agentforce, Snowflake Cortex, ServiceNow Now Assist, Moveworks, Bedrock, Vertex, and n8n, the platform discovers and governs the agents, and runtime enforcement is on the near-term roadmap. Any buyer should ask every vendor exactly which platforms enforce today versus which are promised.

The scope of an autonomous action stays narrow on purpose:

• Revoke a specific OAuth token
• Block a single tool call
• Quarantine one agent so it can no longer invoke tools
• Disable a connector operating in maker mode
• Cut off an unsanctioned route to an external service

Broader moves, such as disabling a user or killing a whole tenant integration, stay in the approve tier by design.

Security teams keep asking one question: by the time my analyst reads the alert, what has the agent already done? The answer is uncomfortable. A confused-deputy scenario, where an over-permissioned agent is manipulated into serving an unauthorized request, can finish a full data pull before anyone opens the queue.

Report-only tooling was built for human-speed threats. People pause. Agents do not. When an over-permissioned Copilot agent starts pulling from a sensitive SharePoint site it should never touch, the blast radius grows with every call. A ticket is not remediation. It is a receipt.

This is the shift customers describe as moving off ghost chasing, where you alert on theoretical config risks with no evidence, and onto runtime truth with enforcement, where you block real observed behavior in place. A machine insider that acts in one second needs a control that acts in one second.

You do not switch autonomous remediation on across the board on day one. You climb a ladder, and you promote individual rules as tuning data earns it.

Choose auto-act only when the rule is deterministic, the action is reversible or narrowly scoped, and the evidence is unambiguous. Everything else stays in approve until it earns promotion. The point of the ladder is that promotion is a decision backed by data, not a default.

The core objection is fair. A probabilistic agent plus an autonomous response can sound like two nondeterministic systems arguing. That is exactly why the guardrails are deterministic and the human owns the policy.

Scope. Every autonomous action targets one entity: one agent, one token, one tool call, one connector. No broad sweeps, no tenant-level kills. If a rule cannot be scoped to a single blast-radius unit, it does not qualify for auto-act.

Reversibility. Prefer actions you can undo with one call. Token revocation, connector disable, tool-call block, and agent quarantine are all reversible. Actions that touch shared state, like deleting records or altering permissions on human accounts, do not belong in auto-act.

Audit. Every action writes a signed record: what fired, why, which evidence supported it, what executed, who owns the rule, and what the reversal path is. That record is what makes the action defensible to your audit committee and to the platform owner whose surface you acted on.

Policy attestation. A rule that runs auto-act is reviewed and signed by a named human owner. The platform enforces the rule. The human owns the policy. Unowned rules produce unowned outages.

Most tools trigger response on theoretical configuration: the agent is set up to reach X, so alert. That produces noise, and noise plus auto-action equals broken production.

The correct trigger is effective access, what the agent can actually reach inside each third-party application after every entitlement resolves. An agent with a broad OAuth scope may in practice touch three objects. An agent with a narrow scope may, through maker mode inheritance, actually reach an admin's entire Salesforce org. The theoretical configuration page will not tell you which is true. Runtime does.

This is the moat. Obsidian's Knowledge Graph correlates the agent's activity to its effective access inside the app: which non-human identity or service account it is running as, which tables, objects, and actions it actually touched, and which human's credentials sit behind the call. Correlating the runner, the maker, and the observed data at runtime is what makes a remediation trigger trustworthy. Without it, you block on configuration guesses, and you either miss the real event or halt legitimate work.

Consider an illustrative flow. An AI coding assistant connects through an external route to what looks like a Salesforce integration. In reality the connector points at a shadow tenant a business team spun up months ago, never sanctioned, holding a copy of production customer records. Production Salesforce runs normally and employees keep working, but every "customer lookup" task the agent runs pulls from the shadow tenant.

Here is the shape of a good autonomous action:

1. The runtime observation shows the agent invoking a Salesforce-shaped tool that resolves to an unregistered domain.
2. The rule matches a deterministic policy: any agent connector reaching an unsanctioned service domain gets blocked.
3. The scoped action fires against that single connector. Not the agent, not the platform, just the route to the shadow tenant.
4. Production Salesforce integrations continue uninterrupted. The agent still performs every other task.
5. A signed record lands with the connector identity, the domain, the observed calls, the rule that fired, and the reversal path.

No downtime for the user. No war room for the security team. The shadow-tenant reach stops and nothing else moves. This scenario is illustrative of the pattern, not a customer quote.

Governance and Audit for Autonomous Actions

An autonomous action is only as defensible as the governance wrapped around it. That wrapper is what turns "the platform blocked something" into an answer an auditor accepts.

• Rule catalog listing owner, severity, action, scope, and reversal path for every auto-act rule.
• Change control on rule authorship, so only authorized people add or modify what the system can do on its own.
• Signed evidence trail for every fired action, tied back to the observed runtime behavior that justified it.
• Reporting on volume, false-positive rate, and reversal frequency, at a cadence your board and platform owners can read.
• Quarterly review to promote, tune, or retire rules against real tuning data.

Auditors ask two questions: who authorized this system to act, and show me the evidence for the last action it took. If you cannot answer both cleanly, the rule is not ready for auto-act. Human oversight does not disappear here. It moves upstream, from clicking "close ticket" after the damage completes to authoring the rules, approving tier-two actions in real time, and reviewing the trail. Leaders at large regulated enterprises are asking for exactly this: bounded automation with signed evidence, not full delegation.