The Shift from Bigger Models to Agentic AI

The Coming Divide

Over the next 12 months, 99% of teams will keep chasing bigger models. But the 1% building agentic systems will quietly take your users, your margins, and your roadmap.

Andrew Ng puts it bluntly: the “giant models” era is fading. The future belongs to Agentic AI — small, specialized models that plan, use tools, check their own work, and collaborate.

This is not about parameter count. It’s about workflow architecture. The winners will be those who control the data, the tools, and the end-to-end automation with trust built in.

Why Agentic AI Wins

Instead of “one big brain,” think of a team of autonomous workers:

• Smaller + cheaper models wired into tools can outperform massive ones on real business tasks.
• JPMorgan reports ~30% cost cuts in operations using agentic workflows.
• The market is growing fast: from $5.1B today to $69B by 2032.

Scale alone can’t compete with tools + memory + roles.

The Agent Stack: New Default Architecture

Agentic systems work because they’re structured. The new “stack” looks like this:

1. Reflection → models critique and fix their own outputs.
2. Tool Use → models call APIs, run code, query databases, search the web.
3. Planning → models break goals into steps, then adapt as they execute.
4. Multi-Agent Roles → researcher, builder, critic, PM — specialists who hand off work.

This plan → act → reflect loop is the engine of leverage.

How to Build Agentic Systems

1. Define the task — choose processes with clear ROI.
2. Wire tools safely — connect APIs, search, code, and databases.
3. Add memory/RAG — let systems recall context and past work.
4. Enforce plan → act → reflect — don’t just generate, evaluate.
5. Set metrics — track task success, error rate, latency, and cost per task.
6. Ship fast — then tighten evaluations and guardrails.

30-Day Starter Plan:

• Pick one high-value process.
• Build a toolchain + role structure.
• Run pilot with task success and cost metrics.
• Iterate, refine, expand.

Applications That Matter

Forget the AGI hype. Ng’s litmus test is clear: “Until companies fire ALL their intellectual workers, AGI isn’t here.”

Agentic AI wins by automating boring, expensive work:

• Document flows
• Insurance claims
• Quality assurance
• Energy management
• Clinical routing
• Factory inspections

Start with a friction audit (cycle time, error rate, rework). Target the top three bottlenecks. Then automate end-to-end with agents.

The Dual-Use Reality

Defense budgets are a growth engine — and agentic AI will be dual-use by default. Key areas:

• Logistics and supply chains
• Predictive maintenance
• Simulation and training
• Secure communications
• Threat detection

Open Source, Edge, and Cost Advantage

• Open Source: Fast, cheap models (~1/10th cost) are a wedge. China and others will press this advantage. Use what ships.
• Edge AI: The next wave is small models running locally.
  • Token costs down >90%.
  • Edge spending heading to $378B by 2028.
  • Small language model (SLM) market: $930M → $5.45B by 2032.

Examples:

• Healthcare: on-device triage + notes, PHI stays local.
• Manufacturing: $99 vision nodes spotting micro-defects in real time.
• Retail: offline assistants that keep serving customers during peak hours.

The Moat: Trust

The only durable advantage is trust:

• Rigorous evaluations
• Real-time monitoring
• Audit trails
• Governance and guardrails

Trust is what makes adoption stick.