AgentOps brings traces, evaluations, cost signals, incidents, and policy checks into one operating view so your team can inspect results, investigate changes, and respond.
The AgentOps Challenge
Application monitoring shows whether the service is available. AgentOps adds the workflow evidence your team selects, such as model outputs, tool calls, approvals, evaluation results, and business outcomes.
Four Pillars of AgentOps
Continuous Evaluation
Run scheduled and event-driven evaluations against the quality, policy, and workflow criteria your team defines.
Semantic Telemetry
Capture the inputs, outputs, tool calls, context identifiers, approvals, and outcomes needed to reproduce and review important events.
Drift Detection
Compare sampled outputs and traces with defined criteria, surface material changes, and route findings for review before they affect more work.
Graduated Containment
Pause the workflow, narrow tool access, require human approval, or move to a fallback path. Keep explicit stop, override, and recovery controls available to operators.
Agentic CloudOps and resilience
Operate agents like production cloud systems.
Apply the same operating discipline to agents that you use for important cloud workloads: observe behavior, evaluate quality, investigate failures, manage cost, and recover from bad releases.
Unified observability
Connect agent traces, tool use, application telemetry, CloudWatch signals, and security events so teams can investigate one workflow instead of five dashboards.
Automated investigation
Use agentic workflows to correlate anomalies, summarize likely root causes, recommend remediation, and preserve the evidence needed for review.
Resilience validation
Bring recovery objectives, failover plans, chaos tests, and operational runbooks into a measurable reliability program for AI-enabled applications.
AI FinOps controls
Track token, model, data, and workflow cost by use case so production agents are governed by unit economics, budgets, and escalation thresholds.
Packaged consulting offering
Regulated Agent Apps Pack
For regulated teams building agent applications, this engagement turns AgentOps into a concrete operating model: app architecture, governance controls, runtime procedures, and evidence for security, risk, compliance, and operations review.
Application design
Agent app architecture
Define the regulated agent apps, tool boundaries, data access patterns, state, handoffs, and approval points before implementation starts.
Controls
Governance and security gates
Design policy checks for IAM, infrastructure, agent tools, secrets, prompt changes, and pull requests before changes reach production.
Operations
Runtime operating model
Map decision traces, service health, cost signals, incident playbooks, escalation rules, and CloudWatch-ready telemetry into one operating view.
Evidence
Compliance evidence
Create risk registers, control mappings, architecture evidence, security review answers, and audit narratives that regulated buyers can inspect.
What gets delivered
Regulated agent app opportunity and risk baseline
Reference architecture for agent apps, tools, data access, and approvals
IAM, secrets, policy-as-code, and deployment gate design
Runtime telemetry and incident operating model
Human approval and escalation workflow for high-impact actions
Compliance evidence package and implementation backlog
AgentOps stands on operational discipline.
AgentOps is the operating discipline around production agents: configured records, evaluations, review rhythms, incident procedures, and escalation paths that support controlled production operation.
Configured records for prompts, tool calls, approvals, and outcomes
Policy and permission checks around production execution
Operational reviews that connect quality, reliability, security, and cost
Incident procedures for drift, unsafe actions, degraded tools, and budget exceptions
Who Needs AgentOps
- Teams operating multiple production agents
- Regulated industries (financial services, healthcare, government)
- Teams where agent-supported decisions have financial or legal consequences
Use AgentOps to review configured quality, cost, failure, and intervention signals, then give security, compliance, and operations teams the records they need to investigate and act.
Frequently Asked Questions
AgentOps is the operating discipline around production AI agents. It combines application health, selected agent traces, evaluation results, tool activity, cost signals, incidents, and policy checks so teams can review behavior and respond to changes.
Traditional monitoring focuses on application health. AgentOps adds workflow-level evidence such as model outputs, tool calls, approvals, evaluation results, and business outcomes. The available detail depends on what the application records and what your data policy permits.
Agent drift is a material change in behavior as prompts, models, tools, data, or operating conditions change. AgentOps compares sampled outputs and traces with defined evaluation criteria, surfaces material changes, and routes findings for review. Coverage depends on the signals, tests, and thresholds configured for the workflow.
AgentOps integrates with cloud logs, application telemetry, agent traces, evaluation harnesses, identity systems, ticketing workflows, and approval systems. On AWS, that can include CloudWatch, Bedrock AgentCore Evaluations, Lambda, Step Functions, and existing security or monitoring tools.
Get AgentOps for Your Agents