What is AgentOps and why it matters
AgentOps is the operating model that keeps AI agents reliable. It defines what agents are allowed to do, how their quality and safety are measured, how cost and latency are controlled, and how changes are shipped without disrupting production. It includes the practices, tooling, and governance needed to run agents safely in production—such as evaluation harnesses, observability, guardrails, cost and latency SLOs, change control, and incident response.
As agents evolve beyond simple chat to perform tasks like querying governed data, filing tickets, drafting emails, and triggering workflows, their power brings both value and risk. Without operational discipline, teams face over-permissive tools, runaway loops, unexpected costs, and privacy concerns. AgentOps offers a practical framework to move fast while maintaining control over quality, safety, and spend.
How AgentOps works
AgentOps follows a lifecycle that helps teams plan, build, evaluate, deploy, and improve AI agents with confidence.
Plan: Start by defining measurable outcomes—such as accuracy, QA pass rate, refusal policy compliance, p95 latency, and cost per task. Document the policies that govern agent behavior: what data is in scope, when the agent must refuse, and which actions require approval. Identify the datasets and documents that will ground decisions, along with a set of “golden tasks” that represent ideal performance.
Build: Design the agent and its environment. Choose your model(s) and retrieval strategy, then define small, scoped tools like create_ticket, query_orders, compare_contracts. Keep inputs and outputs predictable. Structure prompts and guardrails carefully. If your agent uses roles—such as planner, worker, or reviewer—make each role explicit, testable, and easy to disable if needed. Validate everything in a sandbox using synthetic and historical cases.
Evaluate: Create a compact evaluation harness for each workflow, covering happy paths, edge cases, and failure modes. Measure quality, refusal and violation rates, p95 latency, token usage, cost per task, and stability across repeated runs. Add regression suites to catch unintended changes and set pass/fail gates that you’ll consistently enforce.
Deploy and monitor: Roll out agents gradually, starting with shadow mode, then canary testing, followed by progressive exposure. Emit traces for each step and tool call, correlate them to user or service identity, and maintain audit trails. Monitor latency (p50/p95), success and error codes, citation coverage (if using retrieval), token budgets, and cost per task. Ensure rollback and freeze mechanisms are clearly documented and regularly tested.
Improve: Use feedback loops to refine agent behavior. Label outcomes using a simple error taxonomy—such as classification misses, missing context, tool failures, or policy violations. Feed these insights back into prompts, retrieval logic, tools, or training data. Promote changes only after they pass evaluation gates, and maintain a change log. Fine-tune models only when the task is stable and the value of tuning is clear.
How AgentOps compares to DevOps, MLOps, and PromptOps
DevOps focuses on building and deploying software, ensuring infrastructure reliability. Use DevOps when you're deploying deterministic code.
MLOps manages data pipelines, model training, evaluation, and serving. Use MLOps for batch predictions and model lifecycle management.
PromptOps handles versioning and testing of prompts and templates. Use PromptOps when prompt engineering is the core concern.
AgentOps includes elements of all three of the above operating models, but it adds critical layers for agents that reason and act—such as tool scoping, refusal behavior, multi-step traces, grounding and citation coverage, safe rollouts, and incident response. Unlike DevOps or MLOps, AgentOps must manage agents making real-time decisions, using tools, and retrieving context with variable outcomes. It introduces live task evaluation, safety policies, and decision controls that go beyond static code or batch models.
Use AgentOps when workflows involve reasoning, retrieval, and tool use with variable outcomes—especially when actions touch sensitive systems or governed data. If a deterministic script or RPA can handle the task, AgentOps may not be necessary.
Core capabilities of AgentOps
AgentOps provides a control plane for managing AI agents in production, ensuring they operate safely, efficiently, and transparently. It includes observability, safety and governance, and evaluation workflows that support continuous improvement and reliable deployment.
Observability includes traces for each agent step and tool call, with timing and success or error codes. It tracks token usage, cost per task, p50/p95 latency, and stability across reruns. Replay capabilities allow teams to reconstruct decisions, while incident response tools support freezing, rolling back, or rotating secrets quickly.
Safety and governance features include least-privilege RBAC or ABAC on tools and data, short-lived credentials stored in a vault, and data minimization with redaction. Agents follow explicit refusal rules and require approvals for high-impact actions. Full audit trails show who did what, when, and why.
Evaluation and promotion workflows rely on golden tasks and regression suites tied to business metrics. Pass/fail gates assess quality, safety, latency, and cost. Shadow tests, canaries, and sign-offs support safe rollouts, with a documented promotion flow that turns releases into defensible decisions.
How to stand up AgentOps
Phase 0: Define priority workflows and success criteria
Start by selecting two or three workflows with clear business value—such as analytics Q&A, support triage, or a secure IT action. Establish measurable success criteria that stakeholders care about, like “+15% first-contact resolution at ≤2s p95 latency and ≤$0.10 per task.”
Phase 1: Build evaluation harness and golden tasks
Create a small golden set of 30–100 realistic tasks per workflow, including edge cases and negative scenarios like expired tokens or insufficient permissions. Define an error taxonomy to categorize failures, and set promotion gates before refining prompts or tools.
Phase 2: Instrument observability and audit logging
Add spans for agent steps and tool calls, and hash sensitive inputs instead of logging raw values. Correlate logs to user or service identity. Enable replay and confirm that audit logs meet compliance needs without exposing private data.
Phase 3: Apply safety controls and set SLOs
Scope each tool tightly and add approvals where the blast radius is significant. Define token budgets and p95 latency SLOs, and set alerts for drift. Encode refusal rules as enforceable policy—not just prose—and validate them through testing.
Phase 4: Execute a safe rollout
Begin with shadow mode against live traffic, then move to a canary release for a small cohort. Compare performance against baselines and expand only when all gates remain green. Ensure rollback and freeze mechanisms are documented, visible, and regularly tested.
Enterprise use cases
AgentOps applies across a range of enterprise workflows. Below are four common use cases, each with key metrics to track and operational considerations to keep agents safe, effective, and cost-controlled.
Analytics assistant: SQL generation with guardrails
The agent drafts SQL queries against governed data, runs them under a scoped role, and returns results with rationale and citations.
What to measure:
- Accuracy on golden tasks
- Refusal behavior for out-of-scope or unsafe queries
- Violation rate for policy breaches or unauthorized access attempts
- p95 latency per session
- Cost per session
- Citation coverage and grounding quality
Practical tips:
- Keep approvals in place for data-changing operations
- Ensure citations are traceable and grounded in governed sources
Customer operations triage: ticket resolution and escalation
The agent reads incoming support tickets, checks history and entitlements, proposes a resolution, or composes a clean handoff with labels and next steps.
What to measure:
- First-contact resolution rate
- QA pass rate
- Escalation precision
- Average handle time
- Incident counts and time-to-mitigation for failed or misrouted escalations
Practical tips:
- Use labeled golden tickets to benchmark performance
- Track escalation paths to ensure appropriate handoffs
Knowledge workflows: policy comparison and compliance
The agent compares drafts to standards, flags deviations by clause, proposes compliant language, and cites sources.
What to measure:
- Citation coverage
- Contradiction rate
- Redaction/refusal hits due to policy
- Grounding quality and traceability of flagged clauses
Practical tips:
- Encode policy constraints as enforceable rules
- Maintain lineage for all cited documents
Ops automation: secure actions with blast-radius controls
The agent restarts jobs, rotates keys, or files change requests—each behind approvals and rate limits.
What to measure:
- Success rate
- Mean time to recovery (MTTR)
- Blast radius per action
Practical tips:
- Scope tools narrowly and enforce rate limits
- Require approvals for high-impact operations
Best practices
Small, composable tools; deterministic fallbacks; time-boxed loops
Design tools to do one thing well, with clear inputs and outputs. Favor deterministic behavior where possible to reduce surprises. Cap both step count and wall-clock time to avoid runaway loops, and implement backoff strategies to gracefully handle failures.
Data minimization; consent; provenance; policy-as-code checks
Pass only the data each step requires—no more. Keep secrets out of prompts and logs. Respect consent and retention flags throughout the agent lifecycle. Encode policy as code (e.g., refusals, approvals, domain constraints) and test it like any other logic.
Change control: versions for prompts, tools, and retrieval configs
Treat prompts, tools, and retrieval settings like software artifacts. Use branches, diffs, and release workflows. Maintain a changelog to track when performance shifts—and why—so you can debug and iterate with confidence.
Safe rollout: sandbox, shadow, canary, rollback
Roll out agents gradually to reduce risk. Start in a sandbox environment and pass evaluation gates before moving to shadow mode, where agents run silently alongside human workflows. Then deploy to a small cohort in canary mode, applying rate limits and approvals as needed. Always keep rollback buttons and replay logs ready to mitigate issues quickly.
Avoid common pitfalls: scope, audit, cost, and oversight
Avoid unscoped tools that can trigger unintended actions, and ensure audit trails are in place for every decision. Version prompts and retrieval configs to track changes over time. Use golden tasks to benchmark performance, and apply cost controls to prevent budget overruns. Always include human-in-the-loop review for high-stakes workflows—skipping it can lead to missed errors and compliance risks.
How Teradata helps
Teradata enables agents to operate with precision, governance, and flexibility across the enterprise.
VantageCloud Lake serves as the trusted source for the signals and features agents rely on. It offers fine-grained access controls, enforceable freshness, and full data lineage—ensuring agents retrieve only what they’re authorized to use, and that every feature is traceable and policy-compliant.
With Teradata’s Enterprise Vector Store, agents can perform grounded retrieval at request time, pulling the right facts and passages from up-to-date indices. Document lineage is preserved, enabling traceable citations and reducing the risk of hallucination or misinformation.
ClearScape Analytics® ModelOps supports robust evaluation and release workflows. Teams can define golden sets, enforce evaluation gates, monitor for drift, run canary tests, and promote models with full audit trails—so releases are based on evidence, not guesswork.
Teradata’s MCP Server and Bring Your Own LLM (BYO-LLM) capabilities provide secure, flexible orchestration. Agent actions can be exposed through secure connectors, allowing integration with enterprise systems while maintaining control. Teams can select the right model for each workflow—including those requiring long-context handling—and avoid vendor lock-in by maintaining choice and portability.
Key takeaways and next steps
Agents create real value only when they’re operated with intent. Start by picking one workflow, defining success in measurable terms, and building a small golden set that reflects real-world scenarios. Connect governed data, add a few well-scoped tools, and make refusal rules explicit. Monitor p95 latency and cost from day one. Roll out gradually—beginning with shadow mode and canary releases—while keeping guardrails tight. Let metrics, not intuition, guide expansion. Once the workflow stabilizes, consider fine-tuning to optimize latency and cost. If you're scaling across teams, lean on a governed lake like VantageCloud Lake for signals, Enterprise Vector Store for retrieval, ClearScape Analytics® ModelOps for evaluation gates you can defend, and Teradata’s MCP Server with BYO-LLM for securely exposing tools and approvals—without sacrificing flexibility.