The Rise of AI Agents
How AI agents powered by LLMs are pushing a wave of investment, innovation and hopefully utility.
First published Dec 2025 · Updated 4h ago
The Rise of AI Agents
If 2023 was the year of the LLM, thanks to ChatGPT and Llama, (while hallucinate became the word of the year), and 2024 was the year of RAG, 2025 is undoubtedly the year of the agent.
Aaron Levie
AI agents bring democratization to every form of non-deterministic knowledge work. Now, we can dramatically lower the cost of investment for almost any given task in an organization. The mistake that people make when thinking about ROI is making the "R" the core variable, when the real point of leverage is bringing down the cost of "I". Now, we have the ability to blow up the core constraint driving many of these tradeoffs: the cost of doing these activities. Every business in the world has access to the talent and resources of a Fortune 500 company 10 years ago.
Data from the past 12 months confirms that agents are no longer just "promising"; they are effectively effectively rewriting the unit economics of software engineering and business operations.
1. The Economic Thesis: Jevons Paradox in Action
Aaron Levie’s observation above captures the macroeconomic shift perfectly. We are seeing a classic Jevons Paradox in knowledge work: as the efficiency of performing a task increases (driven by lower inference costs and higher agent reliability), the consumption of that task explodes rather than contracts.
- The "Cost of I": Traditional automation required high implementation costs (Capex). Agents lower this "Cost of Investment" by handling non-deterministic edge cases that previously broke rigid scripts.
- Infrastructure Spend: This shift is visible in hard dollars. Enterprise spend on AI infrastructure hit $47.4 billion in the first half of 2024 alone (up 97% YoY), signaling that organizations now view agents as foundational rather than experimental.
2. The Technical Inflection: "Flow Engineering"
What changed in 2025 wasn't just model intelligence, but reliability. We moved from "Prompt Engineering" (optimizing a single turn) to "Flow Engineering" (optimizing the system of turns).
The Benchmarks
The improvement in agentic performance is quantifiable and steep:
- SWE-bench (Software Engineering): In 2023, AI systems could solve just 4.4% of real-world GitHub issues. By early 2025, assisted systems jumped to 71.7% resolution rates.
- Unassisted Autonomy: Even on the more rigorous "unassisted" baselines, agents like Devin moved from ~1.96% to ~13.86% success rates, proving capability in complex, multi-step reasoning without human hand-holding.
3. Deployment: The "Build vs. Buy" Equilibrium
The market has matured into distinct adoption patterns. According to McKinsey's latest State of AI data, 62% of enterprises are now experimenting with agents, with 23% successfully scaling them into production environments.
The adoption is bifurcated:
- The "Buy" Side: Organizations are purchasing "Agentic RAG" solutions for customer experience (CX), where agents don't just answer questions but execute tasks (e.g., processing refunds via APIs).
- The "Build" Side: Developers are utilizing frameworks like LangGraph and the Model Context Protocol (MCP) to build custom orchestration layers. The sweet spot for reliability appears to be agents limited to 5–10 specific tools; beyond this, performance degrades due to context overload.
4. What to Watch: The New Bottlenecks
As we move into 2026, the constraints are shifting from capability to governance.
- Evaluation is the New Gold: You cannot ship what you cannot measure. Traditional "vibes-based" evaluation is being replaced by rigorous testing sets (like SWE-bench Verified) to prevent regression.
- Safety & Compliance: With the EU AI Act entering enforcement phases in 2025, "auditability" is no longer optional. Agents must leave a "paper trail" of their reasoning steps to remain compliant in enterprise sectors like Finance and Healthcare.
- The Human-in-the-Loop: We are not removing humans; we are moving them "up the stack." The human role is shifting from operator to orchestrator, reviewing agent plans rather than executing individual steps.
Conclusion: The agentic revolution is not about replacing workers; it is about giving every worker the leverage of a 10,000-person organization. The cost of "doing" is collapsing, and the value of "directing" is at an all-time high.