The rising use of AI by attackers, and by developers to write code, means security teams are getting more alerts, but less time to clear them. Agentic remediation offers up the use of AI coding agents (that can work around the clock) to find exploitable vulnerabilities, create and test fixes, and document each change. There's no waiting for a developer to write the patch. The agents act, check their own work, and keep a human in the loop to approve what ships.
This guide covers what agentic remediation is, why it matters now, and how it works from find to fix to ship.
What is agentic remediation?
Traditional application security tools raise a ticket and wait. A scanner flags a vulnerability, a developer picks up the alert, and the fix sits in a backlog. Agentic remediation changes the model. The system detects the vulnerability, drafts a candidate fix, tests it, and explains the reasoning behind every change.
The word "agentic" points to how these systems work. An agent observes its environment, decides on an action, and runs that action toward a goal. In security, a set of agents split the job. One set of agents find vulnerable code. Another set of agents generate and validate the fix. Another agent applies the fix and opens a pull request (PR) for human approval. They pass work to each other in a loop, and each pass sharpens the next.
That division of labor matters. When one large engine runs alone, a single blind spot corrupts the whole output. When specialized agents check each other, the system catches its own mistakes before a fix reaches production.
Why remediation is changing
Two reasons: first, attackers now use AI to move faster. This Check Point article documented how attackers used the HexStrike-AI framework to exploit fresh NetScaler zero-days, which shrank the time-to-exploit window. Anthropic reported attackers weaponized its Claude model to write code off data stolen from at least 17 organizations. AI raises both the speed and the stealth of attacks, and it also seeds new malicious packages into open source, which makes malware detection part of the same problem.
Second, AI is writing a large share of the code attackers target. AI generates between 30 and 50 percent of enterprise code, according to the 2025 GitHub Octoverse report and the 2025 Stack Overflow Developer Survey, meaning that code carries more risk per line since it pulls in more than twice as many external dependencies as human-written code. This widens both the attack surface and the remediation gap.
Developers historically don't like to change code they didn't touch, so fixes take longer and ultimately the cost falls on the business. In 2025, breaches involving AI-generated logic ran between four and nine million dollars per incident. This is accelerating the use of automated vulnerability remediation through agents.
How agentic remediation works
Agentic remediation runs as an agent loop with three jobs:
- Find reachable and exploitable vulnerabilities
- Generate and validate a fix
- Apply the fix and open a pull request
Endor Labs builds each stage on the one before it, and keeps a human in the loop for final approval.
Find what is exploitable (reachability)
Scale is the first problem. A mid-sized enterprise might have thousands of repositories. Each repo carries hundreds of vulnerabilities across the code and dependneices, which adds up to hundreds of thousands of vulnerabilities. IDC research suggests that in an enterprise, most teams are dealing with more than 1 million vulnerability alerts. No team can triage that volume, and most of those findings never run.
Reachability analysis fixes the starting point. Instead of ranking vulnerabilities by severity score, reachability tells you which vulnerabilities sit on a code path your application runs. A critical vulnerability in a function you never call is not a fire. A moderate CVE on a live, internet-facing path is. Reachability moves the team from "patch everything" to "patch this first." This is the fastest way to fix the code you write, and to remediate open source vulnerabilities without drowning in CVEs.
Generate the fix (automated patching)
This is where workflows diverge depending on whether the vulnerability is in your first-party code, or in a vulnerability you imported. For first-party code, the fix has to work within the context of the rest of your application.
Generation alone is not enough—if it was, agents could do this on their own. Recursive validation keeps the fixes honest. One agent proposes a patch, a second tests it, and a third confirms that the change introduced no new risk. The loop repeats until the fix holds. This design closes the failure gap that single-pass AI patching created, where a model fixed the primary issue but broke something else. A 2025 review of AI-generated patches on SWE-bench found that 43 percent fixed the target issue yet introduced new failures under adverse conditions. Recursive validation answers that failure mode.
Validation pays off. When teams wrap AI fixes in multi-layer validation, successful patch rates climb from 67 percent to over 90 percent, and false positives drop by more than half (SACR). Fixes arrive with clear reasoning instead of an opaque diff, which builds developer trust. For a deeper look at the engine behind this stage, see how AI agents remediate vulnerabilities at scale.
Ship without breaking changes (upgrade impact analysis)
Open-source dependencies are especially tricky to fix. The most common remediation for a vulnerable dependency is an upgrade, and upgrades can often break builds, especially when maintainers have introduced new functionality since the version your application currently runs.
Upgrade impact analysis closes this gap. It maps the blast radius of an upgrade before you merge, so you see what a version change will break and pick the safe path. This is the back half of the arc: reachability tells you what to fix, patches generate the fix, and upgrade impact analysis ships it. Teams that run automated dependency updates need this step, because automation without impact analysis is how you break production at scale.
Agentic remediation vs. traditional patching
Traditional patching runs on a compliance clock. A scanner produces a list, the list becomes tickets, and the tickets wait for a developer with time and context. Mean time to remediate climbs as the backlog grows, and the backlog rebuilds itself faster than the team can drain it.
Agentic remediation runs on a different clock because it is always on. The system finds the exploitable issue, drafts the fix, tests it, and hands a validated pull request to a human for approval. The human stays in control. The machine does the tedious work.
Traditional patchingAgentic remediationTriggerScanner alert becomes a ticketAgent detects an exploitable issuePrioritizationSeverity scoreReachability and exploitabilityWho writes the fixA developer, laterAn agent, now, for human reviewValidationManual, if it happensRecursive: propose, test, confirmRecordA closed ticketA signed, auditable trailSpeedBacklog timeLoop time
For a full side-by-side, read agentic remediation vs. manual patching.
What to look for in an agentic remediation approach
Many vendors now claim agentic remediation. Few deliver it. Judge an approach on five capabilities.
Reachability gating. The system should validate and prioritize what it works on based on reachability and exploitability in the context of your application code. Importantly, it should also generate a full call graph from your code down into your transitive dependencies to accurately understand how a patch in your code, or an upgrade to a dependency, will impact the running application.
Recursive validation. A fix that skips validation risks iterative degradation, where each AI pass adds new problems. This requires a mix of data flow analysis and integration testing before merging.
Explainability and provenance. The system should explain why it made each change and track that change from commit to deployment. Ask how it records provenance and whether a human can audit the reasoning.
Human-in-the-loop. Autonomy is not the goal. Control is. A human should approve high-risk changes, and the system should make that approval fast.
Watch for red flags: overstated autonomy, no validation layer, or no explainability. Treat a promise of full autonomy with caution. Even strong systems keep a human on high-risk changes. To compare vendors against these criteria, see the best vulnerability remediation tools in 2026, and to set a baseline metric first, read how to measure and cut mean time to remediate.
FAQ
What is agentic remediation? Agentic remediation is a security approach where AI agents find exploitable vulnerabilities in code and open source dependencies, generate and test fixes, and document each change, with a human approving what ships. The agents act and validate their own work instead of raising a ticket and waiting.
How is agentic remediation different from automated patching? Automated patching applies a known update on a schedule. Agentic remediation reasons about the problem: it finds what is exploitable, generates a fix, tests that fix through several agents, and explains the change. Read the full comparison.
Does agentic remediation replace developers? No. It removes the tedious work and keeps developers in control. A human approves high-risk changes, and the system hands over a validated fix with clear reasoning.
Why does reachability matter for remediation? Most vulnerabilities never run. Reachability shows which vulnerabilities sit on a live code path, and models application behavior, so teams fix what attackers can reach and without breaking the running application.
How do you measure agentic remediation? Mean time to remediate is the core metric most teams use. Track how long a fix takes from detection to merge, and compare AI-generated code against human-written code.
Can you trust an AI-generated fix? Trust comes from validation and explainability, and should still require a human in the loop for final approval. Recursive validation tests each fix through several agents, and a provenance trail records why the system made each change, which lets a human audit the result.
What's next?
When you're ready to take the next step in securing your software supply chain, here are 3 ways Endor Labs can help:
