Models change under you
Providers update and retire models on their own schedule. Without regression testing, a routine upgrade can quietly change how your agents behave.
AI Enablement · Run & Scale
Managed AI and AgentOps: keep every agent reliable after go-live
Launching an AI agent is the starting line. Models change, data drifts, costs creep and new use cases pile up. Mirketa runs your agents in production, monitoring, evaluating, fixing and improving them every month, so they keep delivering results your business can count on.
- 24x7 global support
- Any platform: Agentforce and custom agents
- Agents we didn't build welcome too
Agent health · this month
LIVERESOLVED WITHOUT HUMAN87%▲ 6 pts
POLICY VIOLATIONS0— steady
COST PER TASK$0.42▼ 18%
- Healthy
Service triage
- Healthy
Order status
- Tuning
Prior auth intake
- Healthy
Donor outreach
All agents within SLAThis month
Illustrative example. Your scorecard is built around your own KPIs.
Why AgentOps
AI agents don't fail loudly. They drift quietly.
A traditional application either works or it doesn't. An AI agent can be fully available and still give wrong answers, call the wrong tool or cost twice what it did last quarter. Without active operations, quality slowly erodes until a customer or an auditor notices.
Costs creep
Longer prompts, extra tool calls and premium models add up. Token spend that looked small in a pilot becomes a real line item at scale.
Agents multiply
Every team wants its own agent. Without ownership, a backlog and a release process, agent sprawl creates risk faster than it creates value.
What's included
Six AgentOps disciplines, one accountable team
Tools show you problems. Our team fixes them. Managed AI combines the right observability stack with people who know your agents, your systems and your business.
Observability and health
End-to-end traces of every session, tool call and decision, with alerts on errors, latency, loops and escalation spikes.
Quality and regression testing
Golden test sets, automated evaluations and regression runs before every prompt, model or configuration change.
Incident management
Defined severity levels, response times and root-cause analysis when an agent misbehaves, with rollback when needed.
Performance and cost
Prompt and topic tuning, model routing and caching that raise resolution rates while lowering cost per task.
Governance operations
Access reviews, guardrail tuning, audit evidence and autonomy calibration, run as part of your AI trust layer.
New use cases
A managed backlog of enhancements and new agents, prioritized by business value and delivered through a controlled release process.
Operating rhythm
A predictable cadence your leaders can see
Daily
- Health and alert monitoring
- Incident triage and response
- Escalation queue review
Weekly
- Quality sampling and evaluations
- Prompt and topic tuning
- Release of approved changes
Monthly
- Agent scorecard and KPI review
- Cost and usage report
- Backlog prioritization
Quarterly
- Business value review
- Model and platform roadmap
- Autonomy and access recalibration
The AgentOps tool landscape
The right observability stack for your agents, not a new silo
Agent observability has become a crowded market. Platforms now ship native monitoring, APM vendors have added agent modules, and specialist tools focus on tracing and evaluation. We work with what you own and add only what fills a real gap.
| Category | Examples in the market | What it does well | Where it stops |
|---|---|---|---|
| Platform-native observability | Salesforce Agentforce Observability and Testing Center, Microsoft Agent 365, Amazon Bedrock AgentCore Observability | Session tracing, agent analytics, health monitoring and testing for agents on that platform, with no extra tool to buy | Strongest inside its own ecosystem; cross-platform coverage is still emerging |
| APM with agent modules | Datadog LLM Observability, New Relic, Dynatrace | Correlating agent behavior with infrastructure, APIs and databases your agents depend on | Less depth on output quality and multi-step reasoning |
| Agent tracing and evaluation | LangSmith, Langfuse, Arize, Galileo, Braintrust, AgentOps.ai | Deep traces, session replay, evaluation suites and LLM-as-judge scoring, including open-source and self-hosted options | Needs engineering effort to instrument and to act on what it finds |
| AI gateways and cost control | Portkey, Helicone, cloud AI gateways | Model routing, caching, rate limits and token spend tracking | Sees model calls, not business outcomes |
Product names are trademarks of their respective owners and are listed for reference only. Listing does not imply partnership or endorsement. Mirketa is vendor-neutral and does not resell observability software.
Our point of view: dashboards don't fix agents
Every tool above will tell you something went wrong. None of them will rewrite the prompt, retest the agent, get the change approved and release it safely. That's the operational muscle most organizations lack, and it's what Managed AI provides.
One scorecard across platforms
Most enterprises run agents on more than one platform. We bring native telemetry and specialist tools into a single scorecard tied to business KPIs, so leaders see one picture instead of five consoles.
Plans
Managed AI plans that grow with your agent portfolio
Predictable monthly subscriptions based on the number of agents, their criticality and the coverage you need.
Monitor
For organizations with their first agents in production.
- Observability setup on your platform
- Health monitoring and alerting
- Incident response in business hours
- Monthly agent scorecard
Most popular
Optimize
For growing portfolios where quality and cost matter.
- Everything in Monitor
- Evaluation suites and regression testing
- Prompt, model and cost optimization
- Governance operations and audit evidence
- Monthly enhancement capacity
Partner
For business-critical agents across several platforms.
- Everything in Optimize
- 24x7 coverage for critical agents
- Cross-platform scorecard
- Dedicated AgentOps lead
- New agent delivery and quarterly value reviews
Getting started
30-day onboarding, whoever built your agents
We take over agents built by your team, another partner or Mirketa. Onboarding gives every agent a baseline, an owner and a runbook before day-to-day operations begin.
Discover
Inventory your agents, their owners, systems and KPIs, and review existing guardrails and logs.
Instrument and baseline
Enable observability, build evaluation sets and measure today's quality, cost and escalation rates.
Hand over to operations
Agree on SLAs and runbooks, publish the first scorecard and start the operating cadence.
Who it's for
Built for organizations scaling AI beyond the pilot
Agentforce customers
Get full value from Agentforce Observability and Testing Center, with a team that tunes topics, actions and guardrails as your use cases grow.
Multi-platform enterprises
Run agents across Salesforce, Microsoft, AWS and custom frameworks with one operating model and one scorecard.
Regulated industries
Healthcare, financial services and education teams get continuous audit evidence and human oversight built into daily operations.
Lean IT teams
Scale AI without hiring a dedicated AgentOps team. We provide the skills, tools and coverage as a service.
FAQ
Managed AI and AgentOps questions, answered
AgentOps is the discipline of running AI agents in production: monitoring their behavior, evaluating output quality, responding to incidents, controlling cost, enforcing governance and continuously improving them. It extends DevOps and MLOps practices to autonomous agents that reason, call tools and take actions.
Traditional managed services watch uptime, and MLOps watches model accuracy. Agents also need their decisions, tool calls, escalations, policy compliance and business outcomes monitored, because an agent can be fully available and still be giving wrong answers or taking the wrong actions.
Yes. We start with a 30-day onboarding that baselines each agent, instruments it for observability, and documents runbooks, whether it was built by your team, another partner or Mirketa.
Often not at first. Platforms such as Agentforce, Microsoft Agent 365 and Amazon Bedrock AgentCore include native observability. We use what you already have and recommend a specialist tool such as Datadog, LangSmith, Langfuse or Arize only when you run agents across several platforms or need deeper evaluation.
We run your regression evaluation suite against the new model before switching, compare quality, cost and latency, and roll out only when results meet your thresholds. Model changes become planned releases instead of surprises.
We offer monthly subscription tiers based on the number of agents, their criticality and the coverage hours you need, so costs are predictable as you scale.
Your agents are live. Let's keep them getting better.
Tell us which agents you run today and where they live. We'll show you what a managed AgentOps scorecard would look like for them. We respond within one business day.
Need engineering support embedded in your team after launch? Meet our Forward Deployed Engineers.
Get Started
Talk to an AgentOps Expert
Tell us which agents you run today and where they live, and an AgentOps expert will follow up within one business day.
- A specialist follows up within one business day
- A no-obligation conversation about your specific goals
- Answers from someone who actually works in this space
- Your information stays confidential
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