Platform Engineering
Quality at every stage. Resilience in every release. Flawless experiences every time.
What is platform engineering for AI agents?
Once agents can call tools, the question becomes which tools each one can reach. When each team wires its own access, nobody can say which agents can reach which systems, or which ones are running at all.
Platform engineering for agents builds the shared foundation agents run on. It covers orchestration, controlled access to tools and data, identity and permissions, monitoring and the guardrails that keep each agent inside its scope. Teams build on it and do not start from scratch.
Services
What does agent platform engineering include?
Orchestration and runtime
Agents deployed wherever a team happened to choose are hard to scale and hard to recover. We build the runtime that schedules agents, holds their state and recovers a run when a model or an upstream tool fails.
Tool access and identity
An agent that inherits a broad service account can reach systems it should never touch. Each agent gets its own identity and tool access scoped to its task. Tools connect through open standards, and the platform controls which servers an agent may call.
Guardrails and approvals
Some actions should not run without a person. The platform routes actions with side effects to an approval step and pauses the run until someone decides.
Observability and evaluation
Teams cannot fix a failure they cannot trace back to a specific model call or tool action. Tracing records LLM calls, tool invocations and agent decisions, and evaluation runs on every change. Gartner expects 50% of GenAI deployments to have LLM observability investments by 2028, up from 15% in early 2026.
Cloud and container foundation
Agents run on the infrastructure you already operate. We build the agent runtime on your cloud and container foundation, with cost and capacity controls, and platform teams keep one way of running workloads.
Insights
FAQs
Common questions about platform engineering for agents
What is platform engineering for AI agents?
It is the work of building the shared foundation agents run on, covering orchestration, tool access, identity, monitoring and guardrails. Agent teams build on it and inherit the controls.
How do you control what an agent can access?
Each agent has its own identity, and its tool access is scoped to its task. Actions with side effects go to an approval step, and the platform records every tool call.
What is MCP, and does it need governing?
The Model Context Protocol is an open standard for connecting AI applications to tools and data. Every server an agent can call widens what it can reach, and the platform keeps a controlled list of servers and the permissions on each.
How do you monitor agents in production?
The platform traces each run, including model calls, tool calls and decisions, and evaluation repeats after every change. Drift, latency, token cost and tool failures feed the operations tools your teams already use.
Talk to our Platform Engineering Team.
Tell us where your agents run today and who can approve their access. We will reply with the platform gaps we would close first.