AgentOps as a Service
The work that starts after your AI agents reach production — we run the operations.
What is AgentOps and what does it deliver to your enterprise?
AgentOps is DevOps adapted for AI agents. Only 11% of companies run agentic AI in production (Deloitte, 2025), and Gartner expects over 40% of agentic AI projects to be scrapped by the end of 2027 — over cost, unclear value or weak risk controls. OpenSeaPiranha's AgentOps Retainer builds the monitoring, drift detection and eval pipeline that keeps your agent in the 11%.
- ▸Monthly retainer between $5K and $15K with 24/7 anomaly watch
- ▸Eval pipeline, drift detection, and prompt regression testing
- ▸Roadmap: model migration plan and cost optimization on every release
What Is AgentOps?
DevOps for AI
Code goes through CI/CD into production. AI agents need their own loop: prompt drafted, evaluated, shipped, monitored for drift, re-evaluated. AgentOps is the discipline that closes that loop.
Five Pillars: Monitoring, Eval, Cost, Governance, Migration
Production agents need five disciplines running together: latency and cost monitoring, quality evals via LLM-as-judge, token-cost optimization, governance audit logs, and a migration playbook for the next model release.
AgentOps Specialist (the new role)
The job title already exists — Accenture and Scale AI both advertise AgentOps roles — but most mid-market companies can't fill one yet. OSP delivers that role as a service so you don't have to hire one yet.
When Do You Need AgentOps?
Day one of production
The minute an AI agent serves real users, monitoring starts mattering. Half of teams that move from POC to production hit silent failures inside 90 days.
When you run multiple agents
Two or more agents in parallel and the orchestration, race conditions, and cost runaway risks all jump.
When ISO 42001 or EU AI Act audits show up on the calendar
Audit logs, reproducibility, and change management become required — AgentOps gives you those by default.
How OpenSeaPiranha Runs It
Onboarding (two weeks)
Stack review of the existing agent, eval baseline, monitoring stack stand-up (Langfuse or Phoenix plus a custom dashboard), and a runbook handed to your team.
Monthly operation
Weekly eval reports, anomaly alerts, cost-optimization recommendations, and a monthly business review with the founder team.
Quarterly roadmap
When a new Claude or GPT release lands, we ship the migration plan, iterate prompts to v2 and v3, and put a fresh ROI report in front of the executive sponsor.
Pricing Tiers
- ▸1 agent
- ▸Monthly eval report
- ▸Anomaly alerts
- ▸Email support
- ▸3 agents
- ▸Weekly evals
- ▸24/7 alerting
- ▸Slack support
- ▸Migration planning
- ▸Unlimited agents
- ▸Custom dashboard
- ▸On-call SLA
- ▸Dedicated AgentOps lead
- ▸Quarterly QBR
Frequently Asked
How does AgentOps differ from DevOps?
DevOps is built for deterministic code. AgentOps adds the AI-specific disciplines — eval, prompt regression, model migration — that LLM outputs need.
Which LLM providers do you support?
Anthropic Claude (Sonnet and Opus), OpenAI GPT-4 and 5, Google Gemini, Mistral, and self-hosted Llama. Multi-provider strategy is the default, not the exception.
What does an engagement look like?
Three months minimum so the onboarding and the first eval cycle pay back. Month-to-month cancellation opens at month four.
We already have an MLOps team — do we still need AgentOps?
Yes. MLOps owns model training and serving. AgentOps owns prompt and agent behavior. They sit next to each other; they don't replace each other.