Agentic Automation Is Moving Into Core Enterprise Workflows
New AI agent features are landing inside workload automation, ERP, and mainframe-adjacent processes. The winner will be deterministic control, not raw autonomy.
SMH BLOG · FIELD NOTES
Implementation patterns, benchmarks, case studies and adoption playbooks — written by the engineers who actually shipped the work.
New AI agent features are landing inside workload automation, ERP, and mainframe-adjacent processes. The winner will be deterministic control, not raw autonomy.
New AI agent features are landing inside workload automation, ERP, and mainframe-adjacent processes. The winner will be deterministic control, not raw autonomy.
The newest enterprise AI announcements point to a clear pattern: regulated companies want agentic automation where their data already lives.
AI is changing what enterprise software development looks like — not just how fast code ships, but what artifacts exist, what roles matter, and where governance now lives. The shift is structural, and it's already underway.
Composable stacks gave enterprises agility — but every modular surface is now a potential AI surface. Here's how to adopt composability without inheriting AI governance debt.
Data transparency was already a competitive differentiator. In the AI era, it's also a procurement gate — and the enterprises winning trust are the ones treating disclosure as a product feature, not a legal disclaimer.
Most AI pilots stall on the way to production — and the ones that ship without governance artifacts in place often have to be rolled back. Here's the lifecycle model that closes the gap.
Top engineers don't stay for salary alone. In the AI-tooling era, the daily experience of shipping software is also the surface where IP, code provenance, and AI governance get decided.
Open-source AI models are reshaping the enterprise risk calculus. Lower vendor lock-in, real cost advantages — and a governance burden that proprietary buyers don't carry.
Internal developer platforms aren't just about velocity anymore. They've become the enforcement layer for AI policy, audit logging, and model governance at enterprise scale.
A product-led growth playbook for technical founders selling AI into regulated enterprises starts with trust artifacts. The technical-founder advantage is real — if you channel it through governance, not around it.
NEWSLETTER
The patterns and pitfalls we ran into, written up while they're still fresh.