Deploy, manage, and govern hundreds of AI agents across your organization with enterprise-grade platforms built for security, scalability, compliance, and performance.
Building individual AI agents works for proof-of-concepts, but scaling to dozens or hundreds of agents across an organization creates critical challenges:
Each team builds agents differently, with no central visibility, inconsistent monitoring, and fragmented ownership—making governance impossible.
Decentralized agents access sensitive data without unified access controls, audit trails, or compliance frameworks—creating legal and security exposure.
Teams rebuild similar agents independently, duplicating infrastructure, models, and integrations—wasting resources and creating inconsistencies.
Each agent runs on separate infrastructure without resource pooling or intelligent scaling—leading to underutilization or performance issues.
An enterprise AI agent platform provides centralized infrastructure for developing, deploying, managing, and governing AI agents across your organization—with enterprise-grade security, scalability, observability, and compliance built in.
Single control plane for registering, versioning, deploying, and monitoring all AI agents. Track agent inventory, performance metrics, usage patterns, and dependencies in one place.
Enterprise-grade security with role-based access control (RBAC), authentication, authorization, encryption at rest and in transit, and integration with enterprise identity providers.
Cloud-native architecture with auto-scaling, load balancing, and resource pooling. Efficiently handle variable workloads from dozens to thousands of concurrent agent executions.
Comprehensive dashboards for agent performance, cost tracking, error rates, and business impact. Distributed tracing links requests across multi-agent workflows.
Built-in compliance frameworks for GDPR, HIPAA, SOC 2, and industry regulations. Audit trails, data lineage, model governance, and policy enforcement.
Reusable components reduce duplication: shared model hosting, common tool libraries, data connectors, orchestration templates, and integration adapters.
Foundation providing compute, storage, networking, and container orchestration.
Kubernetes clusters with CPU/GPU nodes, auto-scaling, spot instances for cost optimization
Object storage (S3), databases (PostgreSQL, MongoDB), vector stores (Pinecone, Weaviate)
RabbitMQ, Kafka for async communication and event-driven architectures
Redis for session state, API response caching, rate limiting
Core capabilities available to all agents.
Inference APIs for LLMs, embedding models, classifiers with caching and batching
OAuth2, SAML integration, JWT token management, API key rotation
Workflow execution, scheduling, retry logic, state management
Pre-built connectors for enterprise systems (Salesforce, SAP, Workday)
Execution environment where individual agents run.
Isolated execution environments with resource limits and network policies
Catalog of available tools with permissions, rate limits, and usage tracking
Session state, conversation history, working memory for agents
Content filtering, action validation, circuit breakers, approval workflows
Tools for platform administrators and agent developers.
Agent inventory, performance metrics, cost tracking, user management
Agent templates, documentation, SDKs, testing environments
Prometheus, Grafana, distributed tracing (Jaeger), log aggregation (ELK)
Usage patterns, ROI tracking, performance optimization recommendations
Establish core infrastructure, basic agent runtime, and essential platform services.
Add orchestration, monitoring, and shared services for agent developers.
Enhance security, compliance, governance, and management capabilities.
Optimize performance, add advanced features, enable multi-region deployment.
Reusable templates, shared services, and SDKs reduce time-to-market from months to weeks
Resource pooling, auto-scaling, and shared infrastructure reduce per-agent costs by 40%
Centralized security, compliance, and governance minimize regulatory and operational risks
Unified monitoring and management improve reliability, reduce downtime, enable proactive issue resolution
For most organizations, a hybrid approach works best: build on proven open-source foundations (Kubernetes, existing orchestration frameworks) while customizing for your specific requirements, integrations, and security needs. We help you evaluate commercial platforms (AWS Bedrock, Azure AI, Google Vertex) versus custom solutions.
A minimum viable platform (basic infrastructure, agent runtime, monitoring) can be operational in 4-6 weeks. Full enterprise features (advanced security, compliance, multi-region) typically require 3-4 months. We recommend phased rollouts—start with core capabilities, add features as agent adoption grows.
Costs include: infrastructure (compute, storage, networking), model hosting (API costs or self-hosted GPU), development and maintenance, and vendor licenses (if using commercial platforms). At scale, expect $50K-200K monthly operating costs depending on agent count and usage patterns. We optimize for cost-efficiency through resource pooling and caching.
We implement defense-in-depth: network segmentation, encryption at rest and in transit, RBAC with least-privilege, audit logging, vulnerability scanning, penetration testing, and compliance frameworks (SOC 2, GDPR, HIPAA). Regular security reviews and updates keep the platform aligned with evolving threats.
Yes. We provide migration tools and guidance for containerizing existing agents, integrating them with platform services, and refactoring to leverage shared resources. Migration can be gradual—run legacy agents alongside platform-native ones during transition.
Scale your AI agent strategy with enterprise-grade infrastructure. Let's discuss platform architecture, security, compliance, and deployment roadmap for your organization.
Lund, Sweden |