Quick take
The advantage in 2026 isn’t model access. Everyone has that. The advantage is shipping AI features that behave predictably: scoped workflows, measured quality, controlled costs, a rollback path. Expect agents to get practical within guardrails, routing to replace one-model-fits-all, and regulation to become a real deployment constraint. The hype hangover is here. Execution is what matters now.
Prediction posts are dangerous. They age badly. I’ve been wrong before and survived, so here goes.
The conversation has shifted. 2025 proved models can be impressive. 2026 will test whether they are dependable in routine work. The changes that matter will be quieter: fewer surprises, tighter boundaries, and more disciplined economics.
Agents get real – within limits
This is the prediction I feel most confident about: bounded agents will become normal in production. Support triage. Internal ops workflows. Content pipelines. Document processing. The common thread is clear scope, defined tools, and human checkpoints.
The agent architecture that works looks similar everywhere I see it succeed:
- Operates inside a defined workflow with explicit stop points
- Uses tools with strict schemas, not free-form “do anything” capabilities
- Produces intermediate artifacts a human can review – a draft, a classification, extracted fields
- Easy to roll back or disable without breaking the product
A support agent that drafts a reply, proposes a refund category, and attaches relevant policy excerpts? That works. An agent that autonomously changes account settings across multiple systems without review? That will keep failing for boring reasons: permissions, edge cases, accountability, audit.
Full autonomy will remain limited. The hard part isn’t tool use. It’s verification and accountability. Anyone telling you otherwise is selling something.
Routing replaces the monolithic model
One of the clearest patterns I’ve seen: the teams controlling their costs and quality are the ones routing across models. Small model for simple classification. Medium model for drafting. Large model for complex reasoning and synthesis. Choose by task and risk, not by a single default.
Caching and reuse matter too: repeated requests, repeated retrieval, repeated transformations. Teams will treat token spend like any other variable cost and engineer it down.
If your AI feature is expensive today, the fix isn’t “wait for cheaper models.” The fix is to design a system that does less unnecessary work and fails more gracefully. This is basic systems engineering. The AI hype cycle just took a couple of years to remember it.
MCP and the integration layer
I’ve been watching MCP (Model Context Protocol) closely. It’s the kind of boring, practical standard that actually moves the industry forward – a way for models to interact with tools and data sources through a consistent interface. Not revolutionary. Useful.
What excites me about MCP is that it makes the agent architecture I described above more standardized and portable. Tool registries with schemas. Structured inputs and outputs. Less bespoke glue code per integration. Whether MCP specifically wins or another protocol emerges, the direction is clear: tool integration becomes a standard interface, not a custom project.
Enterprise: from experimentation to operations
AI budgets will flow toward integration, governance, and change management. Procurement, security review, and data quality will matter more than novel features. ROI scrutiny will tighten. Projects that can’t show durable value will get cut.
What changes inside organizations is mostly non-technical. Ownership becomes explicit – someone can approve data access, approve risk, and kill a feature. Enablement beats evangelism – internal platforms and reusable components matter more than another demo day. Training becomes practical – teams learn to write specs and evaluate changes, not just “prompt engineering.”
Regulation becomes a deployment constraint
I wrote about this from a NATO-informed perspective – regulation is no longer theoretical. It’s showing up in procurement questionnaires, security reviews, and internal risk sign-off. Teams that build evidence and controls into the system will ship faster than teams that bolt them on later.
The prediction that matters: governance moves onto the critical path. Not as a blocker. As a competitive advantage for teams that do it well.
What probably won’t happen
- Fully autonomous agents everywhere. Verification and accountability are still hard problems.
- Prompt-only reliability. If a feature matters, it needs evaluation, monitoring, and structured interfaces. Not just better wording.
- One model to rule them all. Production systems will route across models because constraints differ by task.
- Frictionless compliance. Regulation doesn’t go away. Teams just get better at building evidence into the workflow.
None of this blocks useful systems. It pushes teams toward discipline. Which is where the value has always been.
What to do right now
If you’re shipping AI, the best moves are unglamorous:
- Pick one workflow with clear value and low blast radius.
- Define success and failure modes in writing.
- Build a small eval set from real examples. Keep it versioned.
- Add a rollback path and monitoring before expanding scope.
- Track cost per successful outcome, not cost per request.
Do those five things and you will be ahead of most teams chasing capability. The advantage in 2026 isn’t clever prompting. It’s building a system that can be operated, debugged, and trusted.
Discipline over heroics. Ruthless focus. Same as always.