AI Agent Infrastructure
Managed infrastructure for running production AI agents at any scale
Autonomous AI agents need more than a model API key. They need sandboxed execution environments, reliable tool integrations, memory that persists across runs, and infrastructure that scales with workload demand. We build and manage all of it.
What We Manage#
Agent Execution Environments#
Isolated sandboxes where your agent code runs safely with controlled resource access.
| Environment | Best For |
|---|---|
| Serverless | Short-lived tasks, event-driven agents |
| Persistent containers | Long-running agents, stateful workflows |
| Dedicated nodes | High-security or compute-intensive workloads |
| GPU-accelerated | Embedding generation, local model inference |
Each environment includes outbound egress control, secrets injection, and CPU/memory limits per agent.
LLM Gateway#
A single endpoint in front of all your model providers.
What it handles:
- Provider routing — Choose model per agent role or cost tier
- Automatic failover — Switch providers on timeout or error without code changes
- Rate limit pooling — Share token budgets across teams and agents
- Semantic caching — Cache identical or near-identical prompts to reduce cost
- Spend controls — Per-agent budgets with hard stops and alerts
Supported Providers#
- OpenAI (GPT-4o, o3, o4-mini)
- Anthropic (Claude Sonnet, Haiku, Opus)
- Mistral AI
- Google Gemini
- AWS Bedrock
- Azure OpenAI
- Self-hosted (Ollama, vLLM, LM Studio)
Persistent Memory#
Agents that remember. State management built for production workloads.
Memory Types#
Vector memory — Semantic search over past interactions, documents, and knowledge bases. Supports Qdrant, Weaviate, and pgvector backends.
Key-value memory — Fast structured storage for agent scratchpads, extracted entities, and task state.
Conversation history — Managed context windows with compression, summarization, and token-budget enforcement.
Shared memory — Memory namespaces accessible across multiple agents in the same workflow.
Tool Execution#
Your agents need to call APIs, run code, search the web, and interact with databases. We host and maintain the execution layer.
Built-in tool categories:
- Web scraping and search
- Code execution sandboxes (Python, Node.js)
- Database query adapters (PostgreSQL, MySQL, MongoDB)
- REST and GraphQL API connectors
- File operations (S3, GCS, Azure Blob)
- Calendar and email integrations
Custom tool registry — Register your own tool endpoints; we handle auth, retry logic, and timeout policies.
Scaling#
| Metric | Details |
|---|---|
| Agent instances | 1 to 10,000+ concurrent |
| Task queue | Priority queues with dead-letter handling |
| Horizontal scaling | Auto-scale based on queue depth |
| Burst capacity | Pre-warmed pools for latency-sensitive workflows |
Security#
- Secrets never exposed to agent code — injected at runtime via vault integration
- Network isolation between agent instances
- Audit log for every tool call and LLM request
- SOC 2 Type II compliant hosting infrastructure
- Data residency options: EU, US, APAC
Getting Started#
We help you move from prototype to production-grade agent deployment. Book a technical scoping call to discuss your agent architecture.
Talk to an agent infrastructure engineer →