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Platform Capabilities

AxonFlow is an enterprise AI control plane that enables organizations to deploy AI agents safely at scale. This page explains what AxonFlow provides and how it solves critical challenges in production AI deployments.

The Problem​

Organizations deploying AI agents face several challenges:

  • Governance Gap: AI agents make decisions in milliseconds, but governance reviews take days
  • Data Access Control: Agents need data from multiple sources, each with different permission models
  • Compliance Requirements: Regulated industries (healthcare, finance) require audit trails and access controls
  • Multi-Agent Complexity: Coordinating multiple AI agents working together is error-prone
  • LLM Provider Lock-in: Switching providers or adding redundancy requires significant engineering

AxonFlow addresses these challenges with a unified control plane that sits between your applications and AI infrastructure.

Sub-10ms Policy Enforcement​

What It Is​

Every AI agent request passes through AxonFlow's policy engine, which evaluates governance rules and makes allow/deny decisions in under 10 milliseconds (P95). This is fast enough that users don't notice any delay, yet comprehensive enough for enterprise compliance.

Why It Matters​

Traditional governance approaches require manual review or batch processing, creating bottlenecks. AxonFlow enables real-time governance at the speed of AI - every request is evaluated against your policies instantly.

How AxonFlow Delivers It​

  • In-Memory Evaluation: Policies are compiled and cached for sub-millisecond evaluation
  • Async Audit Writes: Logging happens in the background, not blocking the request
  • Distributed Architecture: Scale horizontally to handle millions of requests

Use Case​

A healthcare AI assistant processes 10,000 patient queries per hour. Each query is checked against HIPAA policies (patient assignment, minimum necessary rule, PII detection) in under 10ms before the response is returned.

Policy-as-Code​

What It Is​

Governance policies are defined as code (using Rego/OPA), stored in version control, and deployed through your existing CI/CD pipeline. Policies can be updated in real-time without redeploying your applications.

Why It Matters​

  • Auditability: Every policy change is tracked in git with full history
  • Consistency: Same policies apply across development, staging, and production
  • Agility: Update policies instantly when regulations change

How AxonFlow Delivers It​

package axonflow.policy

# Only doctors can access patient records
allow {
    input.context.user_role == "doctor"
    input.context.patient_assigned == true
}

# Automatically redact PII from responses
redact_pii {
    contains(input.response, "SSN")
}

Policies are written in Rego (Open Policy Agent), a declarative language designed for policy decisions. AxonFlow evaluates these policies on every request.

Multi-Agent Planning (MAP)​

What It Is​

MAP enables complex AI workflows by automatically decomposing tasks and coordinating multiple specialized agents working in parallel.

Why It Matters​

Real-world AI applications often require multiple steps: search data, analyze results, generate recommendations, validate output. Running these sequentially is slow. MAP identifies which tasks can run in parallel and orchestrates execution automatically.

How AxonFlow Delivers It​

User Query: "Plan a 5-day trip to Tokyo"

MAP Decomposition:
├── Flight Search (Amadeus)     ─┐
├── Hotel Search (Amadeus)      ─┼─→ Combine → Generate Itinerary
├── Activity Suggestions (LLM)  ─┘
└── Weather Forecast (API)      ─┘

Sequential: ~25 seconds
Parallel (MAP): ~8 seconds

AxonFlow's planning engine analyzes the query, identifies independent tasks, executes them in parallel, and combines results - all while enforcing policies on each step.

MCP Connectors​

What It Is​

Model Context Protocol (MCP) provides standardized, permission-aware access to external data sources. Instead of building custom integrations for each data source, you configure connectors that handle authentication, rate limiting, and access control.

Why It Matters​

AI agents need data from databases, APIs, SaaS applications, and more. Each has different authentication methods, rate limits, and permission models. MCP provides a unified interface with built-in governance.

How AxonFlow Delivers It​

Available Connectors:

  • Databases: PostgreSQL, Redis, Cassandra
  • APIs: HTTP/REST, Amadeus GDS, Salesforce, Slack
  • Data Warehouses: Snowflake

Permission-Aware Access:

permissions:
  - "flights:search:*"      # Can search any flight
  - "hotels:book:domestic"  # Can book domestic hotels only
  - "crm:read:own_accounts" # Can read own customer accounts

Every data request validates permissions before execution. Unauthorized access is blocked and logged.

Service Identity​

What It Is​

Service Identity provides machine-to-machine authentication for AI agents. Each agent or service has a unique identity with specific permissions, separate from user authentication.

Why It Matters​

When an AI agent accesses data on behalf of a user, you need to know:

  • Which service made the request?
  • What permissions does that service have?
  • Is it acting on behalf of an authorized user?

Service Identity answers these questions with cryptographically verifiable credentials.

How AxonFlow Delivers It​

const client = new AxonFlowClient({
  serviceIdentity: {
    name: 'trip-planner',
    type: 'backend-service',
    permissions: [
      'mcp:amadeus:search_flights',
      'mcp:amadeus:search_hotels'
    ]
  }
});

Services authenticate with AxonFlow and receive scoped permissions. User context is passed through, enabling both service-level and user-level access control.

Immutable Audit Trails​

What It Is​

Every AI agent action is recorded in append-only audit logs. These logs capture who made the request, what data was accessed, which policies were evaluated, and what response was returned.

Why It Matters​

Regulated industries require complete audit trails for compliance (HIPAA, SOC 2, GDPR). When auditors ask "who accessed patient X's data on date Y?", you need an immediate, verifiable answer.

How AxonFlow Delivers It​

Captured Information:

  • Request timestamp and unique ID
  • User identity and service identity
  • Query content (with PII redacted)
  • Policy evaluation results
  • Data sources accessed
  • Response summary
  • Latency metrics

Durability Guarantees:

  • Synchronous writes ensure no data loss
  • Multi-AZ replication for disaster recovery
  • Automatic retry on transient failures

Multi-Model LLM Support​

What It Is​

AxonFlow routes LLM requests to the optimal provider based on requirements: compliance needs, cost constraints, latency targets, or availability.

Why It Matters​

  • HIPAA Compliance: Patient data cannot be sent to OpenAI; it must stay in your AWS account (Bedrock)
  • Air-Gapped Environments: Government/defense systems cannot make external API calls (Ollama)
  • Cost Optimization: Route simple queries to cheaper models, complex queries to premium
  • Reliability: Automatic failover when a provider is unavailable

How AxonFlow Delivers It​

Supported Providers:

  • OpenAI (GPT-4, GPT-3.5)
  • AWS Bedrock (Claude, Titan, Llama)
  • Ollama (self-hosted models)

Intelligent Routing:

  • Health-based routing avoids unhealthy providers
  • Cost-aware routing optimizes spend
  • Automatic failover provides redundancy

In-VPC Deployment​

What It Is​

AxonFlow deploys entirely within your AWS VPC. Your data never leaves your infrastructure - not even for policy evaluation or LLM routing.

Why It Matters​

For regulated industries, data residency is non-negotiable. Healthcare organizations under HIPAA, financial services under SOX, and government agencies all require that sensitive data stay within controlled boundaries.

How AxonFlow Delivers It​

  • ECS Fargate: Serverless containers in your VPC
  • RDS Multi-AZ: Database in private subnets with no public access
  • VPC Endpoints: AWS service access without internet exposure
  • Secrets Manager: Credentials never leave your account

AxonFlow runs as containers in your AWS account. You own the infrastructure, the data, and the encryption keys.

Graceful Degradation​

What It Is​

AxonFlow is designed to continue operating even when components fail. If the database is temporarily unavailable, audit logs are written to local storage. If an LLM provider is down, requests route to alternatives.

Why It Matters​

Production systems must be resilient. A database failover shouldn't cause your AI agents to stop working. AxonFlow is architected for 99.9%+ availability.

How AxonFlow Delivers It​

  • Retry with Backoff: Transient failures are automatically retried
  • Local Fallback: Audit logs persist locally if database is unavailable
  • Circuit Breakers: Unhealthy components are isolated to prevent cascading failures
  • Stateless Agents: Any agent instance can handle any request

Real-Time Configuration​

What It Is​

Policies, connector configurations, and routing rules can be updated without redeploying AxonFlow or your applications. Changes take effect immediately.

Why It Matters​

When a new regulation takes effect or a security vulnerability is discovered, you need to update policies immediately - not wait for a deployment window.

How AxonFlow Delivers It​

  • Hot Reload: Policy changes are picked up automatically
  • Feature Flags: Enable/disable capabilities without code changes
  • Dynamic Routing: Add or remove LLM providers without restart

Observability​

What It Is​

AxonFlow provides visibility into your AI agents: request latency, error rates, policy decisions, LLM usage, and cost attribution.

Why It Matters​

You can't improve what you can't measure. Understanding how your AI agents behave in production is essential for optimization and debugging.

How AxonFlow Delivers It​

Metrics Dashboard:

  • Request latency (P50, P95, P99)
  • Error rates by endpoint and policy
  • Throughput (requests/second)
  • LLM token usage and cost

Health Monitoring:

  • Component health endpoints
  • Connector status
  • LLM provider availability

Getting Started​

Self-Hosted (Open Source):

git clone https://github.com/getaxonflow/axonflow.git
docker-compose up
# Access at http://localhost:3000

AWS Marketplace (Enterprise): Deploy via one-click CloudFormation - see Deployment Guide.

Next Steps​