AgentOps Framework
Agent Operations for the Enterprise. We've seen this pattern before. VMs needed orchestration (vSphere). Containers needed orchestration (Kubernetes). AI agents need AgentOps.
The operational discipline for AI agents with agency
Every technology needs operations
AI agents are fundamentally different from previous technologies: they have agency. They reason, decide, and act with varying degrees of autonomy. This requires a new operational paradigm.
VMs → VMware vSphere
Virtual machines needed orchestration for lifecycle, resource management, and governance.
Containers → Kubernetes
Containers needed orchestration for deployment, scaling, and service discovery.
ML Models → MLOps
Machine learning models needed lifecycle management, versioning, and monitoring.
AI Agents → AgentOps
Autonomous agents need identity, policy enforcement, reasoning capture, and governance.
"AI agents have agency. They reason, decide, and act. This requires a new operational paradigm."
The AgentOps platform architecture
A layered architecture for managing agents at enterprise scale with governance, observability, and control.
Click any layer to explore details
Identity Federation
Integrate with your existing identity provider. Support for SAML 2.0, OIDC, and SCIM provisioning. Map enterprise roles to agent permissions automatically.
Secrets & Credentials
Centralized secrets management for API keys, tokens, and credentials. Automatic rotation, audit logging, and just-in-time access for agents.
Network Security
Private endpoints, VPC peering, IP allowlisting. All traffic encrypted in transit. Optional air-gapped deployment for sensitive environments.
Policy-as-Code
Define governance rules in code using OPA/Rego. Version control your policies. Test policy changes before deployment. Automatic enforcement at runtime.
Regulatory Modules
Pre-built compliance modules for financial services (MAS, OCC, BCBS), healthcare (HIPAA, FDA), and government (FedRAMP). Customizable for your jurisdiction.
Approval Workflows
Configure human checkpoints based on action type, risk level, or monetary threshold. Integration with Slack, Teams, and email for approvals.
Agent Registry
Every agent has a unique URN, capability manifest, autonomy level, and accountable owner. Search and discover agents across your organization. Track lineage and dependencies.
Lifecycle Management
Structured progression from development to production. Approval gates between stages. Automatic testing requirements. Blue-green and canary deployment strategies.
Configuration Management
GitOps-style configuration management. Environment-specific overrides. Feature flags for gradual rollout. Instant config updates without redeployment.
SIEM Integration
Stream all agent activity to your SIEM. Pre-built dashboards for Splunk and Datadog. Correlate agent events with your security monitoring. Real-time threat detection.
ITSM & Incident Management
Automatic ticket creation for agent failures. Integration with on-call rotations. Runbook automation for common issues. SLA tracking and reporting.
Enterprise Connectors
Pre-built connectors for 50+ enterprise systems. OAuth, API key, and certificate-based authentication. Rate limiting and circuit breakers built in.
Contextual Authorization
Authorization decisions based on user context, not just identity. Factor in customer segment, transaction value, time of day, and risk signals. Dynamic policy evaluation.
Traffic Management
Sophisticated rate limiting by agent, user, or API. Circuit breakers for downstream protection. Request prioritization for business-critical agents.
Protocol Translation
Expose agents via REST, GraphQL, or gRPC. WebSocket support for streaming responses. Automatic request/response transformation.
Provider Abstraction
Single API for all LLM providers. Switch between OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or self-hosted models without code changes. Consistent interface regardless of provider.
Intelligent Routing
Route requests based on cost, latency, or capability. Automatic fallback when providers have outages. A/B testing between models. Gradual migration between versions.
Cost Control
Set budgets by agent, team, or project. Real-time spend tracking. Alerts before budget exhaustion. Semantic caching to reduce redundant API calls by up to 40%.
Agent Runtimes
Containerized execution environments with CPU/memory limits. Horizontal scaling based on demand. Support for Python, Node.js, and custom runtimes. Warm pools for low latency.
Context & Tools
Integration with Context Engine (ETL-C) for semantic data access. Sandboxed tool execution with timeout and resource limits. Pre-built tools for common operations.
Multi-Agent Coordination
Message passing between agents. Shared state management. Coordination primitives for complex workflows. Support for hierarchical and peer-to-peer topologies.
Reasoning Capture
The "Agent Flight Recorder" — capture full chain-of-thought for every decision. Immutable audit log for compliance. Replay capability for debugging. Evidence for regulatory examination.
Unified Observability
OpenTelemetry-native. Export to Datadog, New Relic, Grafana, or your existing stack. Pre-built dashboards for agent health, performance, and reliability.
Cost Management
Track LLM costs, compute costs, and tool costs by agent. Chargeback to business units. Budget alerts and spend forecasting. ROI analysis by use case.
Human-in-the-loop control matrix
Different actions require different levels of human oversight. AgentOps defines a control matrix based on autonomy level and risk.
| Autonomy Level | Example Actions | Control Required |
|---|---|---|
| Level 1 | Answer product questions | No approval needed |
| Level 2 | Suggest recommendations | Disclosure required |
| Level 3 | Update contact details | Customer confirmation |
| Level 4 | Process applications | Human review queue |
| Level 5 | Override decisions | Senior approval + audit |
Banking-specific extensions
For financial services, AgentOps includes additional modules for regulatory compliance and risk management.
Regulatory isolation
MAS-compliant blast radius containment. Agents can't exceed their risk boundaries.
Reasoning capture for audit
Chain-of-thought persistence with policy citations. Prove why the agent decided what it decided.
Contextual authorization
Tool access varies by customer segment, transaction amount, and risk classification.
Checkpoint orchestration
Mandatory approval workflows for high-stakes decisions. Configurable by policy.
How we help
AgentOps Assessment
$35K
3 weeks. Current agent landscape audit, governance gap analysis, risk assessment, framework recommendations.
AgentOps Design
$100K
8 weeks. Full architecture design, policy framework definition, observability strategy, implementation roadmap.
AgentOps Implementation
$250K+
16-24 weeks. Platform deployment, policy engine setup, observability integration, team enablement.
Ready to operationalize your AI agents?
Start with an AgentOps Assessment to understand your current agent landscape and governance gaps.