AgentOps gives you continuous observability, evaluation, and governance for production AI agents - so you can trust what they do, catch when they drift, and prove compliance.
The AgentOps Challenge
Traditional monitoring tells you IF your systems are running. AgentOps tells you WHAT your agents are deciding, WHY they made that choice, and WHETHER that choice was appropriate. This is the difference between uptime monitoring and decision governance.
Four Pillars of AgentOps
Continuous Evaluation
Not just testing before deployment. Evaluate agent quality in production based on behavior, accuracy, and adherence to policy using the evaluation stack that fits your environment.
Semantic Telemetry
Go beyond logs. Capture the meaning of agent actions - what decision was made, what context was considered, what alternatives were rejected. Full decision lineage.
Drift Detection
Agents evolve as data changes. Detect when agent behavior drifts from defined policies. Alert before small deviations become compliance violations.
Graduated Containment
When an agent goes off-policy, don't just kill it. Throttle autonomy, require human approval, or restrict tool access. Proportional response, not binary shutdown.
Agentic CloudOps and resilience
Operate agents like production cloud systems.
AWS is moving CloudOps toward AI-assisted investigation, full-stack observability, resilience validation, and cost governance. Tactical Edge extends AgentOps into that operating model so production agents can be observed, evaluated, improved, and recovered with the same discipline as mission-critical cloud workloads.
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 buyer-ready evidence.
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 not a dashboard, product dependency, or naming layer. It is the operating system for production agents: the controls, signals, review rhythms, and escalation paths that make agent apps safe to run.
Decision lineage for every prompt, tool call, approval, and outcome
Policy enforcement before and during 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
- Organizations running 5+ production agents
- Regulated industries (financial services, healthcare, government)
- Any team where agent decisions have financial or legal consequences
75% of enterprise leaders say security, compliance, and auditability are the most critical requirements for agent deployment.
Frequently Asked Questions
AgentOps is continuous observability, evaluation, and governance for production AI agents. It goes beyond traditional uptime monitoring to capture what agents are deciding, why they made those choices, and whether those choices align with policy — enabling trust, drift detection, and compliance proof.
Traditional monitoring tells you if your systems are running. AgentOps tells you what your agents are deciding, why they made that choice, and whether that choice was appropriate. This is the difference between uptime monitoring and decision governance.
Agent drift occurs when an agent's behavior gradually deviates from defined policies as data and context change. AgentOps detects drift by continuously evaluating agent outputs against policy benchmarks and alerting before small deviations become compliance violations.
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