Date: 15 June 2026

Agentic development is changing the way software gets built. The shift is bigger than developers using autocomplete or asking a coding assistant to explain a function. Engineering teams are beginning to work with AI agents that can write code, open pull requests, refactor services, generate tests, propose fixes, update infrastructure definitions, and interact with development workflows with a growing degree of autonomy.

Traditional AppSec programs were designed around human-driven development. A developer wrote code, another developer reviewed it, a scanner analyzed it, and security teams prioritized findings after the fact. That model was already under pressure from DevOps, cloud-native engineering, and continuous delivery. Agentic development adds another layer of complexity because software changes can now be produced faster, with less direct human authorship, and across more distributed workflows.

The risk is not simply that AI agents will write insecure code. The bigger issue is that AI agents may generate code without enough organizational context. They may not understand internal authentication patterns, data classification rules, service ownership, business logic, customer-specific constraints, or architectural decisions that are obvious to senior engineers but invisible to a generic model. They may also make changes across repositories, tests, configuration files, APIs, and infrastructure without the same judgement a human team would apply during design review.

At a Glance: Tools to Secure Agentic Development

Best Tools to Secure Your Agentic Development in 2026

1. Apiiro

Apiiro is the strongest platform for organizations that want to secure agentic development with deep software context rather than isolated scanning. Its platform is built around application security posture management, graph-based risk analysis, and code-to-runtime visibility. This matters because agentic development creates risk across connected systems. A generated code change may affect an API, a data flow, a repository, a deployment pipeline, or a cloud service. Apiiro helps teams understand these relationships instead of treating every finding as a disconnected issue.

Apiiro’s Risk Graph model is especially relevant for agentic development because it gives AppSec and engineering teams a way to understand how application components, owners, dependencies, APIs, pipelines, and runtime exposure connect. In an AI-assisted environment, this context becomes critical. Security teams need to know whether generated code affects a critical application, touches sensitive data, changes a customer-facing API, or introduces exposure in production. Apiiro’s strength is that it helps prioritize based on real application context rather than raw alert volume.

The platform is also valuable because agentic development increases the need for governance. AI agents may help generate code, but organizations still need to enforce standards around secure patterns, ownership, review workflows, and remediation. Apiiro supports this by connecting risk visibility with developer workflows and remediation guidance. This allows teams to reduce friction while still maintaining control over how software changes move through the lifecycle.

For enterprises, cloud-native organizations, and security teams managing large application portfolios, Apiiro provides the most complete foundation for agentic AppSec. It is not just a code scanner. It is a platform for understanding application risk across modern software delivery, making it especially well suited for environments where AI-generated code, autonomous workflows, and fast development velocity are becoming normal.

Key Features

• AI-native application security posture management
• Software Risk Graph and contextual prioritization
• Code-to-runtime visibility
• Repository, API, pipeline, and cloud context
• AI-generated code governance support
• Developer workflow integration
• Remediation guidance with ownership visibility
• Strong fit for enterprise agentic development security

2. Arnica

Arnica is a strong choice for organizations focused specifically on governing AI-generated code and preventing insecure patterns before they become part of the codebase. Its approach is closely tied to the reality of agentic development: developers increasingly use AI coding tools, but those tools need security context and enforceable rules if they are going to produce production-ready code safely.

The platform’s Arnie AI suite is designed to embed security and policy enforcement into AI-driven coding workflows. Arnica positions its Agentic Rules Enforcer around applying security rules while developers use AI coding tools, which helps generated code follow organizational expectations from the beginning. This is important because many AI-generated vulnerabilities do not happen because the model cannot write code. They happen because the model does not know the company’s preferred secure patterns.

Arnica is especially useful for teams that want to control how AI-generated code enters repositories. It can help detect what agents missed, apply proactive branch-level scanning, and provide automated mitigation when generated code introduces risk. This makes it relevant for organizations adopting tools such as coding copilots, AI IDE assistants, or internal development agents.

Compared with Apiiro, Arnica is more focused on AI code governance and agentic rule enforcement than broader application security posture management. That makes it a strong complement or focused alternative for companies that need guardrails around AI-generated code but do not yet require a full enterprise ASPM platform.

Key Features

• AI-generated code governance
• Agentic rule enforcement
• AI SAST workflows
• Proactive branch-level scanning
• Automated mitigation support
• Security guardrails for AI coding tools
• Developer workflow integration
• Useful for teams adopting AI coding assistants

3. Aikido Security

Aikido Security is a strong option for teams that want broad developer-first security coverage without operating a large collection of disconnected tools. Its platform combines code, cloud, and runtime security in one system, with AI-powered remediation features designed to help developers fix issues quickly. This makes it especially relevant for organizations that need practical security coverage across fast-moving engineering teams.

Aikido fits agentic development because it focuses on reducing friction. AI-generated code can increase the number of issues entering pull requests, and developers need clear explanations and fixes that do not slow them down. Aikido’s AutoFix capabilities are designed to generate fixes for SAST, IaC, SCA, and container issues, helping teams move from finding to remediation faster. Its platform also emphasizes reducing false positives and giving developers actionable guidance in IDEs and pull requests.

The platform is particularly useful for startups, scaleups, and mid-market engineering teams that want security embedded into daily workflows. It provides a broad set of capabilities across code scanning, dependency security, secrets, infrastructure-as-code, containers, cloud security, and compliance mapping. That breadth makes it attractive for teams that need coverage without complex enterprise deployment cycles.

Aikido may not provide the same level of graph-based enterprise context as Apiiro, but it offers a strong developer experience for teams that want to secure AI-assisted development pragmatically. For organizations where adoption and speed matter as much as coverage, Aikido is a relevant option.

Key Features

• Unified code, cloud, and runtime security
• AI-powered AutoFix for security issues
• SAST, SCA, IaC, secrets, and container scanning
• Developer-first remediation workflows
• IDE and pull request guidance
• Compliance mapping and audit support
• Reduced false-positive workflows
• Strong fit for growing engineering teams

4. Kodem Security

Kodem Security is a strong platform for organizations that want to secure applications from code to runtime using an agentic security model. Its positioning is especially relevant to this topic because Kodem describes its philosophy around combining security researchers and AI to create an agentic security force that continuously learns, reasons, and protects applications from code to runtime.

That code-to-runtime approach matters because agentic development risk does not stop at code review. A generated code change can behave differently once it reaches runtime. It may expose an API, touch sensitive data, interact with identities, or create a path that scanners did not fully understand. Kodem’s model is designed around continuous coverage, which makes it useful for teams that want security to follow applications beyond static analysis.

Kodem is particularly relevant for organizations that want a more dynamic security review process. Instead of only scanning code at one point in time, teams increasingly need systems that reason about application behavior, validate exposure, and keep learning from runtime conditions. That is especially important when AI agents are accelerating software change and creating more frequent updates across services.

Compared with Apiiro, Kodem may appeal to teams looking for a more runtime-connected, researcher-driven agentic security layer. It is especially interesting for organizations that want AI-supported reasoning across application behavior, not only repository-level analysis. For teams thinking about agentic development as a continuous security challenge, Kodem deserves consideration.

Key Features

• Agentic application security model
• Code-to-runtime coverage
• AI-supported security reasoning
• Continuous application protection
• Runtime-aware security review
• Researcher-driven security intelligence
• Application behavior analysis
• Strong fit for dynamic engineering environments

5. Pangea

Pangea is different from the other platforms on this list because it focuses more on AI application security and AI guardrails than traditional code security. That makes it important for agentic development because many teams are not only using AI to write code. They are also building AI-powered products, copilots, internal agents, chat interfaces, and tool-using AI systems that need their own security controls.

Pangea’s AI Guardrail Platform is designed to help teams protect AI applications against AI-specific attacks such as prompt injection, jailbreak attempts, malicious content insertion, and unsafe AI behavior. It can be deployed through gateways or integrated into applications, making it relevant for teams building AI apps that interact with users, tools, data, and internal systems.

This matters because agentic development creates two security challenges. The first is securing code that agents generate. The second is securing the AI agents and applications that engineering teams build. A platform like Pangea helps address the second challenge by adding guardrails around prompts, model inputs, outputs, AI usage, and application behavior.

Pangea is not a replacement for an ASPM platform or a code security scanner. It is better understood as an AI security infrastructure layer for teams building agentic products. For organizations deploying AI-powered applications, internal copilots, or agent workflows that interact with sensitive systems, Pangea can provide an important control layer.

Key Features

• AI application security guardrails
• Prompt injection and jailbreak protection
• Secure AI app and agent workflows
• Gateway and application-level deployment
• AI usage protection and monitoring
• Guardrails for model inputs and outputs
• Protection for workforce AI use
• Strong fit for teams building AI-enabled products

Agentic Development Is Creating a New Security Challenge

Agentic development changes the scale and rhythm of software delivery. A coding assistant helps a developer work faster. An agentic workflow can go further by planning a task, modifying files, creating tests, proposing a pull request, responding to feedback, and sometimes triggering follow-up actions. This creates productivity opportunities, but it also changes where risk enters the software lifecycle.

In a traditional workflow, many security assumptions are tied to human review. Developers understand the business reason for a change. Reviewers know which patterns are acceptable. Platform teams understand how services are deployed. Security teams define controls around repositories, CI/CD, and runtime environments. Agentic development complicates that model because software changes may be produced through a chain of prompts, generated code, tool calls, repository access, and automated actions.

The security concerns are different from standard code scanning. Organizations now need to understand questions such as:

• Which code was generated or modified by AI?
• Did the agent follow internal security standards?
• Did the change introduce risky data handling?
• Did the agent create or modify an API?
• Did it touch authentication, authorization, secrets, or infrastructure?
• Can the organization trace ownership and review responsibility?
• Did the generated code create runtime exposure?

These questions show why agentic development security is not only about vulnerabilities. It is about governance, context, accountability, and control.

A human developer may know that a certain service handles regulated customer data. An AI agent may only see a function signature and nearby files. A senior engineer may know that a specific internal library must be used for authentication. An agent may copy a familiar pattern from public training data. A platform team may know that a configuration change affects production exposure. An agent may treat it as a routine update.

This is why organizations need security tools that understand the software environment around the code, not only the syntax inside the code.

Why Traditional AppSec Was Not Designed for AI Agents

Traditional AppSec tools still matter, but they were not built for agentic software delivery. Static analysis, dependency scanning, secrets detection, and infrastructure-as-code checks all remain valuable. The problem is that they usually operate after code has already been created or changed. Agentic development requires controls that work earlier, faster, and with more context.

The first limitation is speed. AI agents can produce code faster than manual review workflows can scale. If every agent-generated pull request creates another wave of scanner alerts, security teams will quickly face the same backlog problem they already have, only larger.

The second limitation is context. Traditional tools often report findings without fully understanding business impact, runtime exposure, ownership, or data sensitivity. In agentic development, that context becomes essential because teams need to distinguish between low-risk generated code and changes that affect critical systems.

The third limitation is governance. Most scanners do not answer whether AI-generated code followed internal rules. They may find vulnerabilities, but they do not necessarily enforce architecture standards, approved library usage, coding patterns, repository policies, or agent permissions.

The fourth limitation is remediation. Finding a problem is not enough. Developers need safe, reviewable fixes that fit the codebase. AI-assisted remediation can help, but only if the tool understands enough context to avoid introducing new problems.

A secure agentic development program should therefore combine several layers:

• Security checks before code is merged
• Governance for AI-generated code
• Agent permission and repository access control
• Contextual prioritization based on real risk
• Runtime and ownership visibility
• Safe remediation workflows
• Clear human accountability

This is where the market is moving. The best platforms are not just adding AI features to old workflows. They are helping teams rethink how AppSec works when AI agents become part of engineering delivery.

Why Context Matters More Than Static Findings in Building a Secure Agentic Development Program

Securing agentic development requires more than selecting a tool. It requires a program that defines how AI agents can participate in software delivery and how their work is reviewed, governed, and measured.

The first step is visibility. Organizations should know where AI-generated code is being used, which teams rely on coding agents, and which repositories are most affected. Without visibility, security leaders cannot understand the scope of the risk.

The second step is policy. Teams should define which types of changes agents are allowed to make independently and which require stronger human review. A documentation update is different from a change to authentication logic, payment flows, authorization checks, deployment pipelines, or infrastructure access.

The third step is workflow integration. Security checks should run where developers and agents already work: branches, pull requests, CI/CD pipelines, repositories, IDEs, and issue tracking systems. If security controls live outside the workflow, they will be ignored or delayed.

A mature program should include:

• Clear ownership for AI-generated code
• Required review for high-risk changes
• Security rules embedded into AI coding workflows
• Secrets and dependency checks before merge
• Runtime context for prioritization
• Prompt and AI application guardrails
• Remediation guidance developers can trust
• Reporting that measures risk reduction, not only findings

The most important principle is accountability. AI can help write code, but organizations still need humans accountable for what gets merged and deployed. Secure agentic development does not mean blocking AI adoption. It means giving teams the guardrails to use it safely.