AgentOps: we run and improve your AI agents after launch
An AI agent changes when the model, the prompts, your data and your customers change. AgentOps is the monthly retainer where we watch its quality, control its cost and improve it with evidence.
An agent that worked on launch day is not guaranteed to work in six months
The provider updates the model, the catalog changes, a new kind of request arrives, and the token bill creeps up without anyone noticing. With nobody watching, quality degrades quietly and the team finds out from a complaint. AgentOps puts a routine around that: regression evals on every change, monitoring of quality, escalation and cost, guardrails tuned against real failures, and a monthly review with you that ends in concrete decisions. We operate agents we built and existing agents too, after an initial review.
- 01Initial review of the agent: existing evals, logs, permissions and risks
- 02Monitoring of quality, escalation rate, latency and cost per conversation
- 03Regression evals run on every model, prompt or tool change
- 04Guardrail and human-handoff tuning based on real failures
- 05Model cost control: routing by task, caching and limits
- 06Monthly report with the failures reviewed, improvements shipped and next month's priorities
Included
- An initial review of the agent, its logs, its permissions and its existing evals
- Monitoring of quality, escalation rate, latency and cost per conversation, with alerts
- Regression evals on every model, prompt or tool change
- Guardrail and handoff tuning based on real failures
- A monthly review and a written report with the changes shipped and the priorities for next month
Not included
- Building new agents or major new capabilities, which we scope as separate projects
- Model usage fees, which the provider bills at cost
- A guarantee that the agent is never wrong; we measure the error rate, reduce it and design for the cases that remain
- Round-the-clock on-call coverage, unless you agree a stricter incident-response level
A support agent whose answers have drifted
The agent launched well, but the catalog and policies have changed since. We rerun the eval set, find the questions it now gets wrong, update the retrieval and prompts, and show you the before and after numbers.
A model upgrade you are unsure about
The provider has released a newer model and your team wonders whether to switch. We test it against your eval set for quality, latency and cost, and recommend switching, staying or routing by task, with the data to support it.
A token bill that keeps growing
Usage is up and nobody can say which conversations drive the cost. We break cost down by task, add routing, caching and limits where they do not hurt quality, and set alerts for when cost leaves the agreed range.
An agent you inherited
Another team or vendor built the agent, and you now own something you cannot fully explain. We review the code, logs, permissions and any existing evals, then give you a written list of what to fix before we operate it.
- 01
Initial review
We review the agent, its logs, its permissions and the existing evals. If there are no evals, we build them from real conversations to get a baseline before touching anything.
- 02
Monitoring and alerts
We instrument quality, escalation, latency and cost, with alerts for the deviations that matter. Your team sees the same dashboards we do.
- 03
Continuous improvement
Each month we take the most frequent or costly failures, propose changes, test them against the evals and deploy them. Model changes are evaluated before they are adopted.
- 04
Monthly review
We review results, cost and priorities with you. You can pause the retainer or take operations back in-house, and the runbook and dashboards are yours.
Scope, timeline and team are closed during discovery (no cost, 30 minutes).
Support and operations AI agents integrated with your helpdesk, CRM and WhatsApp, shipped with evals, human handoff and AgentOps.
LLM, RAG and MCP integration into your existing software, including a private AI assistant over your documents and systems.
A 2 to 3 week AI readiness sprint: prioritized use cases with ROI, architecture and a fixed-price estimate.
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- AI agent vs RPA: which one fits your processHow AI agents and RPA differ, a process-type matrix to sort your own workflows, and how to combine both without adding risk.