Reliability engineering for mission-critical agent systems. Powered by the AgentControlLayer platform, we bring industrial-grade stability to your AI operations.
Full Stack Observability
SRE practices designed for autonomous systems. SLOs, incident response, and auto-recovery for your agent fleet.
Define service level objectives for your agents. Track error budgets. Get paged when reliability drops below threshold.
When agents fail, time matters. Automated incident detection, escalation, and runbook execution—before users notice.
Test agent resilience before production breaks. Inject failures, simulate rate limits, and verify graceful degradation.
Your AI agents are making business decisions 24/7. A 2-hour outage isn't 'a bug'—it's an incident.
Traditional APM tools don't understand agent workflows. You need observability built for non-deterministic systems.
Most teams find out agents are broken from customer complaints. That's too late. You need automated anomaly detection.
Without SLOs, you can't balance reliability against velocity. Every agent change is a risk with unknown consequences.
Reliability isn't luck—it's engineering. We partner with you to keep agents running.
We analyze your current workflows and identify the highest-ROI opportunities for agentic automation.
Our architects build your agents on the AgentControlLayer platform, ensuring security and scalability.
We deploy to production and train your team on how to manage the Human-in-the-Loop approval flows.
We stay on as your AgentOps partner, reviewing logs and optimizing prompts weekly to prevent drift.
We focus on teams who already ship or operate agents and now need a proper AgentOps control plane.
Product and platform teams adding agents into their SaaS products—support bots, onboarding agents, lead routing, and other embedded workflows.
Central teams that support multiple agent use cases across the business and need one place to control prompts, policies, and observability.
Shops that build agents and workflows for clients and want to offer them as reliable, audited services instead of one-off scripts.
Under the hood, AgentControlLayer is a full AgentOps control plane: a workflow engine, agent identity system, and observability layer that treat agents as first-class principals.
A LangGraph-powered workflow engine with schema-based IO, support for multi-agent patterns, and built-in Human-in-the-Loop nodes so you can pause, review, and resume critical steps.
Agents are treated as their own principals with permissions, histories, and versions—not just prompts in code. This aligns with emerging best practices from Google/Kaggle and others.
Designed to support Promptsmith-style atomic prompt boxes and AI-assisted reviews of prompts and workflows so you can continuously improve quality without losing control.
Common questions about SRE practices for AI agents.
Any measurable behavior: success rate, latency P95, cost per task, human escalation rate, output quality score. We support composite SLOs that combine multiple signals for sophisticated reliability targets.
We use statistical anomaly detection, not hard thresholds. AgentControlSystem learns your agents' normal behavior and alerts when drift exceeds configurable bounds—even for outputs that vary by design.
Yes. Define recovery actions (restart, fallback to backup model, disable and alert) that execute automatically when failures are detected. Runbooks can be triggered based on incident type.
We integrate with PagerDuty, Opsgenie, Slack, and custom webhooks. Incidents flow into your existing on-call rotation. No need to change your current processes.
One AgentOps control plane to build, secure, and observe your agent fleet.
Stop pasting strings into code. Our visual Prompt Builder UI allows you to design, test, and version complex prompts with variables, conditional logic, and model comparisons side-by-side.
Treat agents as first-class citizens with their own IAM roles. Manage permissions, enforce budget limits, and maintain complete audit trails of every decision your AI makes.
Bring DevOps discipline to LLMs. Version control your entire agent configuration—workflows, prompts, and RAG settings. Implement Human-in-the-Loop (HITL) checkpoints before critical actions.
Ready to deploy agents that actually work? We are accepting a limited number of enterprise clients for our Managed Agent Program. Get a custom roadmap, a dedicated AI Architect, and access to the AgentControlLayer platform.