AGENT PLATFORM ARCHITECTURE AND DEPLOYMENT
Agent platform architecture and deployment
We design and deploy the software foundation for agents, including orchestration, integration, memory and monitoring, with clear operating responsibilities.
- Governed MCP connectors to your systems of record, least-privilege by default
- Checkpointing and state persistence, so failed runs resume instead of restarting
- Budgets, circuit-breakers, and a trace on every action from day one
The concerns a demo never has to handle are the ones production lives on.
A prototype proves an agent can do the task. A platform proves it can do the task safely, repeatedly, and under watch.
The runtime handles least-privilege access, resumable state, cost limits, and an audit trail, the things that decide whether your security and SRE functions will let an agent near a system of record. It gives the systems from Multi-Agent System Design and the agents from Custom Agent Development a place to run, and it enforces the limits set in Agentic AI Governance and Risk Management.
An agent nobody can watch is an agent nobody can trust.
Every platform we build shares the same governed layers.
Adapted to your environment and cloud. The two layers that most often decide whether a platform holds up are state persistence and observability.
A deployed runtime, wired together and instrumented before real work runs.
- A deployed agent runtime on your cloud: orchestration, integration, memory, and observability wired together.
- Governed MCP connectors to your systems of record with least-privilege scopes.
- Checkpointing and state persistence, so runs are resumable and long tasks survive failures.
- Guardrails, budgets, and circuit-breakers that stop runaway loops and cap cost per task.
- Observability and eval instrumentation: traces, cost, latency, and quality on every action.
- CI/CD for agents, so prompts, tools, and policies ship through a tested, reversible pipeline.
Five steps, from cloud assessment to operate or transfer.
Assess
Your cloud, security posture, and any existing agent runtime.
Design
The platform layers against the reference architecture, sized to your risk.
Deploy
Orchestration, governed connectors, memory, and observability on your infrastructure.
Instrument
Guardrails, budgets, and evals before any agent handles real work.
Operate or transfer
We run it as a managed pod or hand it to your team with runbooks.
The runtime stack we build on.
We are framework-pragmatic, choosing tools for production control rather than demo speed, and we deploy cloud-native on your platform.
We use LangGraph and Semantic Kernel where production-grade control and human-in-the-loop matter most, and managed runtimes where they fit your cloud commitments. MCP is our default for connecting agents to tools and data, one governed standard beats a drawer of bespoke integrations. It shares foundations with Scaled GenAI and AI Platforms.
Engage the platform work in the shape that fits where you are.
A managed pod or transfer can be staffed through Dedicated Teams.
What runs on the platform, and what governs it.
Multi-Agent System Design
The coordinated systems the platform orchestrates and keeps resumable.
Custom Agent Development
The agents that need a governed place to run in production.
Discovery and Process Mining
The evidence that decides which processes are worth putting on the platform.
AI Enablement and Change Adoption
Getting your SRE, security, and delivery teams ready to operate agents.
Agentic AI Governance and Risk Management
The limits the platform's guardrails and gates enforce at runtime.
Agentic AI
The parent practice this platform work belongs to.
What platform leaders ask us first.
Often, yes. A managed runtime covers orchestration, but you still need governed connectors, observability, guardrails, and a deployment pipeline around it. We use the managed pieces where they fit and build the rest to your standards.
Yes. We build cloud-native on AWS, Azure, or GCP, using your identity, networking, and security controls rather than standing up a parallel stack.
Budgets and step limits per task, circuit-breakers on repeated tool calls, and model routing so cheap models handle routine steps. Cost is a first-class metric in the observability layer, not an end-of-month surprise.
Let's design the runtime that takes your agents from prototype to governed production on your cloud.
Orchestration, governed connectors, resumable state, and observability, wired and instrumented before real work runs.