Service / Agentic AI as a Service

AgentOps & Managed Run

Operate production agents with SLOs, observability, incident management, cost control and continuous improvement through AAIOS.

Business context

What this capability solves

Agents are probabilistic software services. They require monitoring not only for uptime but also for action quality, policy compliance, cost, drift and business outcome.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Operational Monitoring

Success/error, latency, tool calls, queue depth, retries and environment health.

Quality Monitoring

Task completion, escalation, user feedback and evaluation regression.

Cost / Capacity

Token/model cost, tool usage, concurrency, budgets and efficiency.

Incident Management

Agent containment, kill switch, safe-mode, rollback, forensic traces and escalation.

Change Management

Model/prompt/tool/version changes with test gates and release evidence.

Continuous Improvement

Backlog from failures, feedback, new tools and business-process changes.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Agent Portfolio

Use cases, agent owners, risk tiers, process boundaries and value hypotheses.

AAIOS Control Plane

Agent registry, orchestration, identity, policy, approvals, memory, tool/model gateways and evaluation.

Enterprise Execution

Models, RAG, APIs, SaaS, databases, workflows and sandboxed agent runtimes.

AgentOps

Observability, SLOs, cost, incident handling, kill switch, evidence and continuous optimization.

Controls & governance

Least-privilege agent/workload identity
Human approval for high-impact actions
Approved tool schemas and transaction validation
Data classification/DLP at model and tool boundaries
Memory retention and deletion policy
Comprehensive traces and action provenance
Evaluation gates before production
Kill switch, rollback and incident escalation

Priority use cases

Managed production agent
24x7/extended-hour agent operations
High-volume customer agent
Business-critical workflow
Multi-agent platform operations

Key deliverables

AgentOps dashboard
SLO/SLA model
On-call/escalation runbook
Incident playbook
Cost report
Improvement backlog
Monthly service review

Integration considerations

AAIOS
Enterprise IAM/workload identity
APIs and SaaS systems
Data platform and RAG/vector stores
Workflow/BPM/ITSM
SIEM/SOAR and observability
GRC/evidence systems
Model endpoints/gateways

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. SelectPrioritize workflows by value, feasibility, risk and data/tool readiness.

2. EngineerDesign agents, tools, RAG, policy, human checkpoints and AAIOS runtime.

3. AssureSimulate, evaluate, red-team, approve and define SLOs.

4. OperateRun through AgentOps, manage incidents and continuously optimize.

Outcome and KPI framework

Task successEscalation rateAgent incident MTTRCost per completed taskSLO attainmentRegression failure rate