Securing Agents
Understanding Highflame’s Security & Monitoring Architecture
This section introduces the core security and monitoring model that powers Highflame. Understanding these patterns will help you choose the proper integration approach and maximize value from Highflame when protecting your AI agents, applications, and workflows.
Highflame supports three primary integration patterns, depending on where and how you want security, visibility, and enforcement applied.
1. Unified Agent Gateway
For MCP, LLM, and Multi-Agent Traffic
The Highflame Unified Gateway is a centralized control point for securing live AI traffic. It acts as a secure intermediary that inspects, monitors, and enforces policy on requests before they reach LLM providers or downstream AI services. Instead of AI agents and applications communicating directly with model providers, traffic is routed through Highflame.
Why use the Unified Agent Gateway
This architecture provides strong, centralized security without requiring complex logic to be embedded in every application.
Before Highflame
Applications call LLM providers directly
Each new agent or service requires custom security and policy logic
Limited visibility into prompts, responses, and usage
Switching providers or adding fallbacks requires code changes
With the Highflame Unified Agent Gateway
By routing AI traffic through the Unified Gateway, you can:
Define security policies, guardrails, and access controls once, and enforce them across all AI traffic
Gain a complete, real-time view of AI interactions, performance, and costs across the enterprise
Automatically apply protections such as prompt injection detection and policy enforcement
Easily switch between LLM providers or configure fallbacks without modifying application code
Secure MCP, LLM, and multi-agent workflows consistently
2. Trace Ingestion Endpoints
For Post-Execution Monitoring, Visibility, Detection, and Alerting
For environments where routing live traffic through a gateway is not feasible, Highflame provides Ingestion Endpoints. These endpoints ingest AI traces, logs, and metadata from agents, frameworks, and platforms, enabling Highflame to perform security analysis post-execution.
Using ingestion, Highflame can:
Analyze traces to detect security threats, policy violations, and anomalous behavior
Generate real-time alerts for detected risks
Construct an agentic context graph that captures relationships between agents, tools, models, users, and actions
Provide centralized observability across distributed or third-party AI systems
Support monitoring for managed platforms where inline interception is not possible
This pattern is ideal for retroactive security analysis, compliance monitoring, and ecosystem-wide visibility.
3. Guardrail APIs
For Direct Agent Protection
Highflame also exposes Guardrail API endpoints that can be embedded directly into AI agents and applications. This pattern allows developers to call Highflame’s security controls at critical points in an agent’s lifecycle, such as before prompt execution, after model responses, or during tool invocation.
When to use Guardrail APIs
Protect individual agents without routing all traffic through a gateway
Enforce policies in custom or constrained environments
Apply targeted controls such as content filtering, policy checks, or safety validation
Integrate security directly into agent logic while keeping enforcement centralized
Guardrail APIs give teams fine-grained control over where and how protections are applied, while still benefiting from Highflame’s centralized policy engine.
Choosing the Right Pattern
These patterns are not mutually exclusive. Many organizations combine them:
Use the Unified Gateway for core AI services and production traffic where real-time enforcement is critical
Use Ingestion Endpoints for third-party tools, SaaS platforms, or latency-sensitive systems where enforcement is not critical
Use Guardrail APIs for embedding security directly into the application logic embedded into agent flows
Together, they provide flexible, layered security across the entire AI lifecycle from model calls to multi-agent workflows.
What's Next?
Learn on the AI Gateway page how to configure Providers and Routes.
See the process in action in the Quick Start Guide for Agent Developers.
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