AI Agent Development & Deployment
Production AI Agents
Built, Deployed, and Operated on GCP & AWS
We don't build AI agent demos. We architect, containerize, deploy, and operate autonomous agent systems that handle real workflows — from DevOps incident response to compliance monitoring to customer ops.
Anyone can vibe-code an agent prototype.
Enterprise requires something harder.
The gap between a demo agent and a production agent is the same as the gap between a script and a system. Production means state management, error recovery, observability, security, cost controls, and graceful degradation — not just a prompt and an API key.
What We Build
Four Agent Archetypes
Operations Agents
Incident response, runbook execution, alert triage, and postmortem generation. Agents that keep systems running without waking the on-call engineer.
Data Pipeline Agents
ETL orchestration, data quality monitoring, schema migration, and anomaly detection. Agents that keep your data flowing and trustworthy.
Customer Ops Agents
Ticket routing, response drafting, escalation detection, and knowledge base maintenance. Agents that improve resolution time and customer satisfaction.
Compliance & Audit Agents
Policy enforcement, audit trail generation, regulatory change monitoring, and documentation validation. Agents that keep you compliant automatically.
Our Stack
The Tools Behind Our Agents
LangGraph
State machine orchestration for multi-agent workflows
Anthropic Claude
Primary LLM backbone
MCP (Model Context Protocol)
Standardized tool integration
GCP Cloud Run / AWS ECS
Serverless, per-request agent deployment
GKE / EKS
Kubernetes for multi-agent systems and long-running workflows
pgvector
Production vector memory on Cloud SQL or RDS
LangSmith
Full trace observability for every agent run
Terraform
Infrastructure-as-code for repeatable deployments
GitLab CI + Kaniko
Automated build and deploy pipeline
Our Process
From Discovery to Production
Discovery
Audit your workflows, identify the highest-ROI agent opportunities, and define success metrics.
Architecture
Design the agent system: state machines, tool integrations, memory strategy, and deployment topology.
Build
Implement, test, and iterate. Every agent run is traced and observable from day one.
Deploy
Containerize, ship to Cloud Run, ECS, or Kubernetes (GKE/EKS), wire up monitoring, alerting, and rollback.
Operate
Ongoing optimization, drift detection, model upgrades, and knowledge transfer to your team.
Scale
Expand to new workflows, teams, and use cases. Replicate proven patterns and grow your agent ecosystem.
Proof of Execution
RunBook Co-Pilot
Our flagship AI agent product — a production system for DevOps and SRE teams that subscribes to alert streams, reasons through incidents, executes remediation, and auto-generates postmortems. The proof that we build what we sell.
Built with: LangGraph · Anthropic Claude · MCP · GCP Cloud Run · pgvector · LangSmith · GitLab CI
Ready to build production AI agents?
We offer a free 45-minute technical assessment for qualified companies. No sales pitch — just an honest evaluation of your AI agent opportunities.