Why Heeler Auto-Remediation Is More Than “AI Fixing Code”

AI coding assistants are increasingly capable of generating fixes and suggesting dependency upgrades. But on their own, AI tools struggle in real-world dependency remediation. Their outputs are non-deterministic, meaning the same input can produce different recommendations. They lack full visibility into your dependency graph, including direct, transitive, bundled, and first-party libraries. And they are constrained by training data that does not reflect your actual environments or internal codebases.

Heeler brings the context and deterministic analysis required to make AI effective for security. It builds a complete understanding of your software and uses that ground truth to determine fixability, the safest upgrade path, and the true impact of a change before any automation occurs.

Heeler does use an LLM. But the value is not that an LLM suggests a fix. The value is that the LLM operates inside a deterministic, validated, and auditable system that is centralized, scalable, and repeatable across the organization.

The Problem With “Just Update the Dependency”

Teams need answers to much harder questions that directly affect stability, delivery velocity, and risk. They need to understand what the safest viable version actually is, whether a change will introduce breaking behavior, and whether multiple vulnerabilities should be addressed together or independently. Just as importantly, teams need to know if a remediation is even fixable right now, and how to prioritize work based on business context, environment, and exploitability.

These are not questions an LLM can reliably answer on its own.

Heeler exists to answer them deterministically, and then operationalize those answers inside developer workflows.

What Heeler Auto-Remediation Does Differently

Heeler performs deterministic analysis of dependency change impact and fixability before any automation occurs. That analysis is then enforced through a centralized and consistent remediation process, rather than leaving decisions to individual developers leveraging various AI coding tools.

The result is fewer alerts, safer upgrades, and remediation that scales across teams and repositories without sacrificing customer trust.

Noise Reduction Through Unified Remediation

Instead of treating every vulnerable package as a separate problem, Heeler groups related vulnerabilities into a single, coherent remediation and creates one unified ticket with full context. That ticket includes the safest upgrade path, links back to the originating changesets, and clear visibility into which repositories and environments are affected. PRs produced by the agent are automatically linked back to these tickets for full traceability and auditability.

This dramatically reduces alert fatigue, avoids duplicated work, meets compliance requirements, and allows developers to focus on meaningful fixes instead of managing hundreds of disconnected Jira issues.

False Positive Reduction With a Real Dependency Graph

Heeler builds a comprehensive dependency graph for each repository, capturing not just direct dependencies, but also transitive dependencies, first-party libraries, and bundled components.

By correctly handling constrained and unpinned versions, Heeler generates a precise SBOM that reflects what is actually in use, not what might be inferred from manifests alone. This removes large classes of false positives that plague traditional SCA tools and gives teams a much clearer view of their true supply chain risk.

The result is fewer wasted cycles chasing irrelevant findings and far higher trust in what the platform reports.

Remediation Guidance That Avoids Breaking Changes

Heeler does not simply recommend “update to latest.”

For each vulnerable package, Heeler identifies the specific version that delivers the highest security impact while minimizing development risk in the context of all the uses of that library That means selecting a version that remediates all relevant vulnerabilities without introducing unnecessary changes or incompatible upgrades.

Each recommendation is backed by breaking-change detection using deep dependency graph analysis, allowing teams to understand the downstream impact of a change before it is made. This shifts remediation from trial-and-error to informed decision-making.

First-Party, Direct, and Transitive Awareness

Not all dependencies are equal, and treating them as such creates confusion and misaligned ownership.

Heeler distinguishes between direct dependencies, transitive dependencies, and transitives introduced through first-party libraries. This allows remediation work to be traced back to the correct repository and owning team, even when issues originate in shared internal components.

For organizations with common libraries and platform teams, this clarity is critical to avoiding duplicated effort and misrouted fixes.

Validated PRs, Not Blind Automation

Automation without validation introduces risk. Heeler is explicit about this.

Only remediations that are determined to be Auto-Fixable through fixability analysis are eligible for automation. When triggered, Heeler’s agent applies the recommended remediation, runs compilation and validation checks, and evaluates whether any first-party code is affected by the upgrade. A feedback loop allows the agent to correct errors until validation is completed.

The agent then generates a fully documented, validated pull request that is linked directly to the remediation ticket. Developers review and merge the PR using their existing workflows, with a high degree of confidence in the outcome.

Why Heeler Is Not “Just an LLM”

Heeler does not repackage an LLM and call it dependency management.

Before any agent executes a change, Heeler performs deterministic analysis using proprietary technology to determine fixability, remediation guidance, and the safest upgrade path. The agent then executes a multi-step workflow using this verified context, rather than relying on probabilistic reasoning.

The LLM operates inside the system, not instead of it.

Why LLM-Only Approaches Fall Short

LLM-based approaches struggle in real-world dependency management because they lack several fundamental capabilities required for safe remediation.

First, determinism matters. LLM outputs are probabilistic, meaning identical inputs can produce different recommendations. That variability is unacceptable for security, governance, and large-scale remediation programs where consistency and auditability are required.

Second, LLMs lack system ground truth. They do not inherently know your full dependency graph, which environments are affected, or how first-party libraries propagate dependencies downstream. Without this context, recommendations are educated guesses at best.

Finally, LLMs cannot reliably assess fixability. They cannot reliably determine whether a remediation is safe to apply, identify the minimum version that resolves all vulnerabilities, or evaluate whether transitive upgrades will break upstream consumers.

Heeler addresses all of these gaps by building the complete dependency graph, determining the minimum-change and safest remediation, validating fixes through real compilation checks and listening to available unit tests, and executing remediation through a governed, repeatable workflow.

The Real Value of Heeler

Heeler does use an LLM. But the value is not that an LLM suggests a fix. The value is that AI operates inside a deterministic, validated, and auditable system that understands your software the way security teams need it to. That is what makes remediation centralized instead of ad hoc, scalable instead of fragile, and repeatable instead of dependent on individual developers.

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