Not "is it up" - is it still right.
The full trajectory of a specific case, on demand.
Prompt, model, tool, data or source system.
Reversal path for every consequential action.
Attributed per workflow, not pooled.
What is AgentOps?
Is it running? Is it still accurate?
Is it still doing the right thing, the right way, at an acceptable cost?
AgentOps is the set of practices for releasing, monitoring, evaluating, governing and improving AI agents running in production.
It extends MLOps and LLMOps to handle what changes when software can take actions rather than only produce outputs: releases where a prompt edit is a deployment, evaluation that checks the sequence of steps rather than only the final answer, incident response where the incident may already have written to a system of record, and rollback that has to consider what the agent did as well as what it is.
The short version: DevOps asks whether the system is running. MLOps asks whether the model is still accurate. AgentOps asks whether the agent is still doing the right thing, the right way, at an acceptable cost, and what to do in the hour after it was not.
DevOps, MLOps, LLMOps, AgentOps.
Each generation added a new failure mode that the previous discipline had no way to see.
| DevOps | MLOps | LLMOps | AgentOps | |
|---|---|---|---|---|
| Core question | Is it running? | Is the model still accurate? | Is the output still good? | Is it still doing the right thing, the right way? |
| Unit of release | Code | Code and model version | Code, model and prompt | Code, model, prompt, tools and permissions |
| What you test | Functions and integrations | Predictions against a test set | Output quality against examples | The full trajectory of steps, not only the result |
| How it fails | Loudly. Errors and alerts. | Quietly. Accuracy drifts. | Quietly and fluently. Confident and wrong. | Quietly, fluently, and it has already acted. |
| Cost profile | Predictable infrastructure | Training plus serving | Per token, variable | Per step, multiplied by the number of steps |
| Rollback means | Redeploy the last version | Revert to the previous model | Revert the prompt or model | Revert the agent and unwind what it did |
| Worst case | Downtime | Poor predictions | A wrong answer to a user | A wrong action in a system of record |
Six ways agents degrade without alerting anyone.
None of these show up on an uptime dashboard. All six are detectable if you are looking for them.
Outputs slowly get worse. No errors, no alerts. The first signal is a complaint or an audit finding.
Detected by: scheduled golden-set evaluationThe agent calls the right tool with slightly wrong arguments, or calls the wrong tool for an edge case it has not seen.
Detected by: trajectory evaluationA source system adds a field, a document template changes, a new product line appears. Retrieval quietly starts returning the wrong context.
Detected by: retrieval quality monitoringA small edit to handle one edge case degrades three others. Nobody notices because nobody re-ran the full set.
Detected by: regression gates on releaseThe agent hands back more and more cases. Throughput looks fine, but the business value is quietly draining away.
Detected by: escalation rate monitoringVolume grows, context windows expand, a model is deprecated and traffic shifts to a pricier one. Finance finds out at month end.
Detected by: per-workflow cost telemetryRight answer. Wrong path.
Output evaluation checks whether the final result was correct. For an agent that is not enough, because the steps it took along the way had consequences of their own.
"The answer was correct, so it passed"
A credit check agent returns the right decision. Evaluated on output only, this run scores as a success and nothing is flagged.
- Retrieved applicant record - correct
- Queried production credit API four times, not once
- Read a document outside the case scope
- Skipped the policy check step entirely
- Returned the correct decision - passes
"The path is checked too"
The same run is scored on the sequence of steps. Three problems surface that output testing could never have seen, before they become an incident.
- Step sequence checked against expected path
- Tool call count and arguments validated
- Data access checked against case scope
- Required policy steps confirmed present
- Output correctness checked as well
Four extra API calls is a cost problem. Reading out-of-scope data is a compliance problem. Skipping the policy check is an audit finding waiting to happen. All three were invisible to a test that only looked at the answer.
What Our AgentOps Services Cover.
Release management
In an agentic system a prompt edit is a deployment, a tool permission change is a deployment, and a model version bump is a deployment. All of them go through the same controlled path as code.
What it covers- Versioning for prompts, tools and agent definitions
- Regression gates that must pass before release
- Staged rollout and canary on a slice of volume
- Approval path for permission and scope changes
Evaluation
Golden sets of real cases with verified correct answers and expected paths, run on a schedule rather than at release only, so degradation is found by us rather than by your users.
What it covers- Output correctness against verified answers
- Trajectory scoring of the full step sequence
- Retrieval quality and grounding checks
- Golden set maintenance as the business changes
Observability
Every run reconstructable end to end. When someone asks why a specific case was handled a particular way, the answer takes minutes rather than an investigation.
What it covers- Full trace of steps, tool calls and retrieved sources
- Searchable run history at transaction level
- Latency, throughput and step-count telemetry
- Retention aligned to your audit requirements
Incident response
Severity defined by what the agent did, not by whether the system was available. An agent writing wrong data into a system of record is a severity one even while everything is technically healthy.
What it covers- Action-aware severity model
- Containment: pause, restrict scope or drop autonomy level
- Impact assessment across affected transactions
- Post-incident review and corrective action
Rollback & reversal
Two different problems. Reverting the agent is straightforward. Unwinding what it already did in downstream systems is the part that has to be designed before go-live rather than improvised during an incident.
What it covers- Version rollback for agents, prompts and tools
- Reversal paths for consequential actions
- Identification of affected records in scope
- Correction workflow with human approval
Cost & capacity control
Agent cost is per step multiplied by steps taken, so a small change in reasoning behaviour can move the bill significantly. Cost is monitored per workflow and treated as an operational metric, not a finance surprise.
What it covers- Cost per transaction and per workflow
- Model routing and right-sizing per step type
- Semantic caching and retrieval tuning
- Budget ceilings with anomaly alerting
Decades Of Data & AI Delivery
The scale and experience behind every engagement, from strategy through production.
An available agent doing the wrong thing is still an incident.
Conventional severity models are built around availability. This one is built around consequence.
Thresholds and response times are agreed per workflow based on risk tier and business criticality.
What Our AgentOps Solutions Monitor.
Reported monthly to the business owner, to technology, and to risk and audit, each in the terms they use.
- Output accuracy
- Trajectory conformance
- Retrieval grounding
- Citation correctness
- Decline rate
- Escalation rate
- Human override rate
- Step count per case
- Tool call patterns
- Policy exceptions
- Availability
- Latency per step
- Throughput
- Queue depth
- Failure and retry rate
- Cost per transaction
- Cost per workflow
- Model mix
- Cache hit rate
- Value against baseline
Real Outcomes, Delivered at Scale
See how we help enterprises apply AI to improve operations, decision-making and customer experiences.
Transforming Dealer & Customer Support with AI
An AI-powered knowledge platform streamlined dealer and customer support by delivering faster, context-aware responses and reducing manual effort.
AI Virtual CFO Platform for Smarter Financial Decisions
A multi-tenant AI platform consolidated financial data, automated reporting, and delivered advisor-level insights for thousands of concurrent users.
From Customer Requirements to Engineering-Ready Designs
An AI-powered platform converted customer requirements into engineering-ready design inputs, reducing design iterations and accelerating production readiness.
Decision Intelligence Agents for Financial Institutions
A unified AI finance engine integrated ERP and credit models to deliver real-time liquidity insights and automated working capital decisions.
Decisioning
Technology Supporting AgentOps Development and Operations.
Our AgentOps practice spans tracing and evaluation frameworks, golden-set management, cost telemetry, release and version control, and alerting, wired into the same foundation as the agents themselves.
The IX AI Foundry control plane provides tracing, evaluation and cost telemetry by default, and we work with your existing observability and incident tooling rather than replacing it.
Python
TypeScript
Go
LangChain
AutoGen
CrewAI
LangGraph
GPT-4
Claude
Llama 3
Mistral AI
Pinecone
ChromaDB
FAISS
Weaviate
CrewAI
AutoGPT
BabyAGI
AutoGen AI
AWS Bedrock
Vertex AI
Azure OpenAI
NVIDIA DGX
DeepSpeed
TensorRT
ONNX
Guardrails AI
Moderation API
Voyager
SWARM AI
CAMEL
SK-LLM
OpenCV
YOLO
TF VisionAgentOps Questions
The set of practices for releasing, monitoring, evaluating, governing and improving AI agents in production. It extends MLOps and LLMOps to handle what changes when software takes actions: releases where a prompt edit is a deployment, evaluation of the step sequence rather than only the output, incident response where the incident may already have written to a system of record, and rollback that considers what the agent did as well as what it is.
MLOps asks whether the model is still accurate. LLMOps adds prompt and output quality management. AgentOps adds everything that follows from the system being able to act: tool and permission versioning, trajectory evaluation, action-aware incident severity, reversal of completed actions, and per-step cost control.
Scoring the sequence of steps an agent took, not only the final answer. An agent can reach the correct result while calling a production API four times instead of once, reading data outside the case scope, or skipping a required policy step. Output-only testing cannot see any of that.
Scheduled evaluation against maintained golden sets, trajectory conformance scoring, retrieval quality monitoring, and watching escalation and override rates. A rising escalation rate is usually the earliest signal that something upstream has changed.
Severity is set by consequence rather than availability. A wrong action in a system of record is a severity one even while the system is healthy: pause the agent, contain, identify affected records, begin reversal, and notify the business and risk owners. Reversal paths are designed before go-live rather than improvised during the incident.
Two different things. Reverting the agent, prompt, tool set or model version is straightforward. Unwinding actions already taken in downstream systems is harder, which is why every consequential action needs a defined reversal path designed during the build.
Agent cost is per step multiplied by the number of steps taken, so a change in reasoning behaviour can move the bill significantly even at flat volume. Add growing volume, expanding context windows and model deprecations shifting traffic to pricier models. Cost is monitored per workflow rather than pooled into a cloud bill.
Yes, and arguably more so. A single agent handling a real workflow with no evaluation and no tracing is the highest-risk configuration there is, because nobody is watching and nobody would know. The discipline scales down; the need does not disappear.
Yes. That is a significant part of the service. We start with a health check covering evaluation coverage, tracing, permissions, reversal paths and cost, then close the critical gaps before taking on steady-state operation.
Tracing and evaluation frameworks, golden set management, cost telemetry, release and version control, and alerting. The IX AI Foundry control plane provides these by default, and we work with your existing observability and incident tooling rather than replacing it.