The Agentic Manager
By Solomon Chika Okafor
We are no longer in the era of simple chatbots.
In 2026, businesses are deploying autonomous teams of AI agents.
The challenge is no longer building AI — it is managing it.
1. The Rise of the "Agent SRE"
AI agents do not always behave as expected.
They drift, fail, and sometimes go completely off-track.
This has created a new role: the Agent SRE (Site Reliability Engineer).
- Monitoring agent behavior in real time
- Debugging broken workflows
- Preventing cascading failures across systems
- Ensuring reliability of autonomous processes
Fixing an AI agent is not like fixing code.
You are fixing decisions.
2. ROI Checklist for 2026
Many businesses rushed into AI automation — and failed.
Nearly 40% of agentic projects are not delivering value.
The problem is not the technology.
It is poor process design.
- Undefined workflows before automation
- Over-reliance on generic AI tools
- Lack of human oversight
- No clear performance metrics
Automation only works if the process itself works.
3. Domain-Specific Models (DSMs)
Generic AI models are powerful — but not always accurate.
Businesses are now shifting toward domain-specific models.
- Legal AI trained on case law
- Medical AI trained on clinical data
- Architecture AI trained on design systems
- Higher accuracy in specialized tasks
This shift is about precision, not scale.
The best AI is no longer the biggest — it is the most relevant.
Reality Check
| Area | Opportunity | Limitation | Impact |
|---|---|---|---|
| Agent SRE | Reliable automation | Skill shortage | Business continuity |
| Agentic ROI | Cost savings | Poor implementation | Operational efficiency |
| Domain Models | High accuracy | Training cost | Industry transformation |
Final Thoughts
The future of work is not human vs AI.
It is human managing AI.
- Design processes before automating them
- Monitor agents like employees
- Invest in specialized intelligence
Because in the end:
Automation without control is risk.
