AgentFence provides a unified security layer for AI agents, covering policy enforcement, access control, and anomaly detection. It offers SDKs for Python, Node.js, and REST APIs to easily integrate with your LLM applications. With real-time monitoring dashboards and detailed audit trails, compliance teams gain full visibility into agent behavior. Customizable policies let you define allowed actions, data use rules, and user roles. Automated alerts notify stakeholders of policy violations, while historical logs support forensic analysis and regulatory reporting.

Ceylon AI leverages large language models to translate your infrastructure needs into executable code and API calls. Simply describe the environment or service you want in plain English, and Ceylon AI crafts Terraform scripts or invokes cloud provider APIs directly. It supports AWS, Azure, GCP, Kubernetes, and more. Built-in drift detection alerts you when live infrastructure diverges from your declared state. Cost estimation, security scans, audit logs, and policy enforcement ensure your environments remain compliant, optimized, and reliable throughout their lifecycle.

CompliantLLM provides enterprises with an end-to-end compliance solution for large language model deployments. By integrating CompliantLLM’s SDK or API gateway, all LLM interactions are intercepted and evaluated against user-defined policies, including data privacy rules, industry-specific regulations, and corporate governance standards. Sensitive information is automatically redacted or masked, ensuring that protected data never leaves the organization. The platform generates immutable audit logs and visual dashboards, enabling compliance officers and security teams to monitor usage patterns, investigate potential violations, and produce detailed compliance reports. With customizable policy templates and role-based access control, CompliantLLM simplifies policy management, accelerates audit readiness, and reduces the risk of non-compliance in AI workflows.

Nuzon-AI provides a Python-based agent framework that lets you define tasks, manage conversational memory, and extend capabilities via plugins. It supports integration with major LLMs (OpenAI, local models), enabling agents to perform web interactions, data analysis, and automated workflows. The architecture includes a skill registry, tool invocation system, and multi-agent orchestration layer, allowing you to compose agents for customer support, research assistance, and personal productivity. With configuration files, you can tailor each agent’s behavior, memory retention policy, and logging for debugging or audit purposes.

Integry AI Agent is a no-code integration and automation platform using advanced language models to let users design, deploy, and manage intelligent digital workers. Leveraging over 1000 pre-built connectors to apps like Salesforce, Slack, HubSpot, and QuickBooks, it transforms natural language instructions into end-to-end workflows without manual API coding. Users define triggers, actions, and conditional logic via a guided interface or chat-based prompts. AI-driven suggestions speed up flow creation, while real-time dashboards offer visibility into task execution, error handling, and performance analytics. Integry also enforces enterprise-grade security and compliance with role-based access control and audit logs, ensuring scalable automation for any team.

Secure Agent Augmentation provides a Python SDK and set of helper modules to wrap AI agent tool calls with security controls. It supports integration with popular LLM frameworks like LangChain and Semantic Kernel, and connects to secret vaults (e.g., HashiCorp Vault, AWS Secrets Manager). Encryption-at-rest and in-transit, role-based access control, and audit trails ensure that agents can augment their reasoning with internal knowledge bases and APIs without exposing sensitive data. Developers define secured tool endpoints, configure authentication policies, and initialize an augmented agent instance to run secure queries against private data sources.

Sys-Agent provides a secure, self-hosted environment where users issue natural language instructions to perform system-level tasks. It connects with AI backends like OpenAI, local LLMs or other model services, translating prompts into shell commands, file operations, and infrastructure checks. Users can customize prompts, define task templates, scale through Docker or Kubernetes, and extend functionality via plugins. Sys-Agent logs all actions and offers audit trails to ensure transparency and security.