Supply chain
Case study
Agentic AI operations
AgentOps by KPI Partners
Agents that survive production: Reliable, affordable and auditable on any platform.
Release gates, model routing, observability and governance that keep every agent accurate and in budget long after go-live.
Delivered natively on
Why agents stall after the pilot
Agents pass the demo, then stall in production
The model is seldom the culprit. The operating model around it is.
Costs multiply out of sight
One question can become six or more billed model calls.
Quality drifts without a release
A model or data change degrades answers with no release from you.
Changes ship on a spot check
Prompts, tools and routing change outside version control.
No one can explain an answer
No trace, version or audit record when someone asks why.
The offering: AgentOps by KPI Partners
One operating discipline, built for every agent you run
Five controls, delivered natively on your platform.
Building gets an agent working. AgentOps keeps it working as data, models and users change, whether your first agent is in build or already live.
- Release and evaluation gateEvery change is versioned, scored and approved before it goes live. Rollback takes one step.
- Model routing and cost controlRoutine questions go to small models, repeats come from cache, spend stops at a hard cap.
- End-to-end observabilityEvery answer is traced with its model, tools, cost and version.
- Governance in the platformLeast-privilege tools, user identity on every query and human approval for consequential actions.
- Expert feedback loopYour experts review only the hard cases, and each release is tested hardest where the last one stumbled.
4%
Of model spend carries more than a third of all questions
1 in 5
Questions answered with zero tokens
~1 sec
To answer a routine question, against six to nine on a large model
1 step
To roll back a bad release
How it works
One governed foundation, from first question to audited answer
A platform-agnostic reference architecture: Five layers serve each request, three planes run across all of them.
1
Experience
- Chat surfaces
- Embedded apps
- API and MCP
- Single sign-on
2
Agent runtime
- Supervisor
- Domain sub-agents
- Tools
- State store
3
AI gateway
- Guardrails
- Model routing
- Semantic cache
- Budgets
4
Model pools
- Small pool
- Large pool
- Frontier fallback
- Any host
5
Data and knowledge
- Governed tables
- Semantic layer
- Hybrid index
- Docs and ontology
Release planeImmutable versions, evaluation gate, alias rollback, drift tests
Observability planeSpan-level traces, LLM judges, expert review, audience views
Governance planeLeast privilege, user identity, audit table, human approval
Controls live in the platform, not in the prompt. API: Application programming interface. MCP: Model Context Protocol. LLM: Large language model.
Go deeper
Read the AgentOps blog
The release cycle, model routing design and platform service map, explained by our AgentOps practice.
Why KPI Partners
A new discipline, delivered by a team enterprises already trust
Governed and human-approved
Role-based access on every query, guardrails on every answer and human approval on consequential actions.
Native to your stack
Snowflake, Databricks, AWS or Microsoft Azure, with agents, traces and evaluations kept next to your data.
Delivered by KPI Partners
AWS DevOps Competency holder, trusted by 300+ enterprises. You extend a partner you know, not a new vendor.
Proof
Agents in production, delivering measurable outcomes
Up to 20%
Lower model cost than a single baseline model, across about 1,000 queries, for a multimodal assistant that helps site engineers troubleshoot heavy machinery. Answers got faster too.
Industrial manufacturerManufacturing
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Put your agents on an operating discipline before the next release
It is far cheaper to build the operating model before go-live than after the first surprising invoice. See where cost, quality and governance risk sit in the agents you run today.