LEVEL 4 OF 5
Agent Manager
Multi-agent orchestration with governance and QA
“You manage the manager: agents run the work, humans run the system”
You feel like you're running a team, not using a tool. Confident but cautious—you want to scale but worry about losing control. You're learning to trust the system.
Level 4
What This Stage Looks Like
At Agent Manager, the business stops supervising individual agents and starts supervising an agent operation. A manager layer (human-led or AI-led with rules) routes work across specialized agents, sequences steps, enforces approvals, handles exceptions, monitors performance, and maintains auditability.
This is where AI becomes a workforce instead of a toolset.
The operating model: Humans set goals and constraints. The manager layer assigns and supervises agent execution.
Operating Model at Level 4
What AI Is Doing
Receiving tasks/intake (requests, events, triggers)
Deciding which agent(s) to use
Passing context across agents (so work isn't repeated)
Enforcing rules (approval gates, required checks)
Escalating edge cases to humans
Producing end-to-end deliverables reliably
What Humans Are Doing
Managing priorities and performance (not doing the steps)
Approving where required (high-risk actions)
Auditing outputs and handling exceptions
Tuning policies, budgets, quality thresholds
Expanding coverage to more workflows
Artifacts You Produce
What you create at this level
Orchestration logic (routing rules, queues, priorities)
Approval gates (what requires human sign-off)
Quality controls (QA checks, sampling, scorecards)
Audit logs and traceability ("who/what did what, when, why")
SLAs and performance dashboards for agents
Key Metrics
How you measure success
1
Throughput (tasks completed per week/month)
2
SLA adherence (on-time completion)
3
Exception rate + time-to-resolution
4
Quality score trend over time
5
Cost per completed task / per workflow
6
Human hours saved and where humans now spend time (exceptions vs production)
Graduation Criteria
What it takes to reach Level 5
The digital operation is stable, measurable, and auditable
Exceptions are manageable and safety gates are proven
You can confidently "push buttons" that cause actions—because controls work
The business case for physical automation is clear (ROI + safety + feasibility)
Common Pitfalls
What keeps organizations stuck
No governance → agents create chaos faster than humans can fix
No QA → trust dies → adoption dies
No cost controls → runaway usage costs
"Fully autonomous" without exception handling → operational disasters
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