Ops Pillar · AI Ops

Keep every model and agent healthy in production.

AI Ops gives you telemetry for models, AI agents, prompts and executions in one place: live performance and cost, quality and feedback signals, a studio to build and govern agents, and the access controls to run them safely, then hands it all to Sentinel AI to act.

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Agent & model telemetry
Quality & feedback
Agent studio
Knowledge grounding
Governed access

Agent operationsLast 7 days

RequestsSteadyLive

Cost / 1kTrackedIn budget

Response timeLowHealthy

FeedbackPositiveRising

Reliability

Most executions passing

Pass rate steady across agents and models.

A failure pattern to review

A cluster of failures shares one root cause.

Token spend trending down

Prompt and routing changes lowering cost.

What is AI Ops

Operations for the AI you put into production.

Teams ship models and AI agents, then lose sight of how they perform, what they cost, and whether users trust them. AI Ops brings telemetry for models, AI agents, prompts and executions into one place, with live performance and cost, quality and feedback signals, a studio to build and govern agents, knowledge grounding, and role-based access, so what you launch stays healthy, affordable and accountable.

Agent & model telemetry

See performance, cost and reliability, live.

Track request volume, token usage, cost per thousand requests, response time and success rate across every agent and model, filtered by agent and time window. When executions fail, failure-reason analysis groups them by cause so you fix the pattern, not the symptom.

Requests, tokens, cost per 1k and latency in real time
Pass and fail counts with failure-reason analysis
Per-agent filtering across any time window

Performance analysisAll agents

PASSexecutions completing successfullyMost

TOKENSusage & cost per 1k trackedIn budget

LATENCYaverage response timeLow

FAILgrouped by failure reasonReview

Quality, feedback & drift

Know whether users actually trust the output.

Performance is only half the story. AI Ops captures user satisfaction and quality signals, lets you review real conversations end to end, and surfaces feedback engagement so you catch quality drift, not just outages, before it erodes trust.

User satisfaction and quality signals per agent
Full conversation review, request to response
Feedback engagement trends to catch quality drift

Feedback & conversationsQuality

POSITIVEhelpful responses, thumbs upRising

FLAGGEDresponses users marked unclearReview

CONVOopen a full conversation trailReplay

DRIFTquality trend versus last periodTracked

Agent studio

Build, connect and version the agents that run.

An agent studio to manage everything an AI agent needs: onboarded applications, versioned prompts, tools, skills, and the MCP and agent-to-agent servers that let agents call systems and each other. Ship changes deliberately, and see their effect in the telemetry.

Onboarded applications, prompts and skills, versioned
Tools plus MCP and A2A (agent-to-agent) servers
Prompt and configuration changes reflected in telemetry

Agent studioComponents

Onboarded applicationsPromptsToolsSkillsMCP serversA2A servers

Knowledge & grounding

Keep agents accurate and on-domain.

Ground agents in your own knowledge and language. A knowledge cache, hints, named entities and synonyms keep responses accurate, consistent and speaking your organization's terms, so quality is engineered in, not left to chance.

Knowledge cache for grounded, current answers
Named entities and synonyms for your domain language
Hints to steer agents toward the right behavior

Knowledge & groundingConfiguration

CACHEknowledge cache, grounded answersFresh

ENTITIESnamed entities recognisedMapped

SYNONYMSyour domain vocabularyApplied

HINTSbehaviour guidance for agentsActive

Governed access & audit

Least-privilege for every agent and data source.

AI needs the same guardrails as any other production system. Role-based permission groups control exactly which services, APIs, data sources, agents and knowledge caches a user or agent can reach, and configuration and data-import audits keep a complete, reviewable trail.

RBAC permission groups over services, data and agents
Least-privilege access, provisioned at scale
Configuration and data-import audit trails

Access & governanceRBAC

GROUPpermission groups by roleActive

SCOPEservices, data sources, agents, knowledgeScoped

POLICYleast-privilege enforcedOn

AUDITconfiguration & data-import trailComplete

06Real-world use cases

Built for the models you already run

These are not demo scenarios. Each one is a situation AI Ops teams meet in real operations, and each ends the same way: an investigation that starts at the evidence and finishes with a decision someone can defend.

An agent starts costing too much

Cost per thousand requests tracked per agent against volume and latency.

Overspend caught the same day

Answers quietly get worse

Satisfaction and feedback trends surface drift before the complaints do.

Quality drift caught early

A prompt change goes out

Prompts, tools and skills are versioned, and the effect shows in telemetry.

Ship deliberately, see the result

An agent needs least privilege

RBAC groups scope which services, data and agents it can reach.

Audit-ready by default

07Business perspective

What it is worth outside the ML team

AI Ops is an engineering capability with a business ledger. The entries that matter are controlled ai spend, protected user trust, faster safe iteration and governed ai access, and it moves all of them.

Controlled AI spend

Token cost tracked per agent before it reaches finance.

Protected user trust

Quality drift surfaces before adoption starts to fall.

Faster safe iteration

Prompt and model changes ship with their effect visible.

Governed AI access

Least-privilege and audit trails across agents and data.

08The numbers

The cost of diagnosis, summed honestly

Worked example for an enterprise with a mature model estate. Every figure shows the sum it came from; during a pilot we substitute your own inputs.

Specialist hours on diagnosis

3,024 hrs

14/mo × 6 hrs × 3 specialists × 12

That is standing headcount

1.7 FTE

3,024 hrs / 1,800 productive hrs per FTE-year

Annual cost of diagnosis

$333k

3,024 hrs × $110/hr fully loaded

Released if diagnosis shortens

$150k

Modelled at 45% reduction, not a measured result

AI Ops sees. Sentinel acts.

AI Ops does more than chart a rising cost or a failing agent. Every signal, performance, cost, quality, feedback and access, feeds Sentinel AI, the intelligence component at the core of Opstral, which resolves issues through governed, reversible Action Tickets.

Roll back a bad prompt, reroute an over-budget model, revoke an over-broad permission, every step explained with citations and fully audited.

Observe
AI Ops correlates performance, cost, quality, feedback and access signals.
Investigate
Sentinel AI finds the root cause across agents, prompts and models and picks the fix.
Act
ProcBot executes the approved MOP, roll back, reroute, rescope, through a reversible Action Ticket.
Optimize
Sherlock validates the outcome and feeds the learning back to keep quality and cost in line.

See AI Ops on your own agents.

Book a walkthrough and see live telemetry, quality signals, an agent studio and governed access, on models and agents that look like yours.

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