Tech boffins and marketers throw around the word “agent” with even more abandon than “digital” a few years ago. It’s all good fun until your trading floor reboots or your claims platform flatlines.

Ask 10 tech gurus what an AI agent is, and you’ll get at least 15 answers. But for business leaders, you only need one: an agent is a piece of AI-powered software that gets work done.

AI agents are the bots that help teams do work – writing code, testing applications, processing claims, redesigning legacy apps, identifying new drug combinations, triaging customer issues, rebalancing supply chains, and more.

Simply, an agent is code – AI-enabled software aligned to a goal. It senses the environment, reasons, decides, adapts, and acts. It may use other tools, collaborate with humans, or delegate work to other agents. You’ll often find them working together in a constellation of agents aligned to specific tasks in a business workflow.

In short: agents are the mechanism for AI to actually do work with business impact rather than being complex math and massive data sets. They are already reshaping how we work.

At Amazon, CEO Andy Jassy shared that GenAI tools have already saved 4,500 developer-years and generated $260 million in annualized efficiency gains. At Google, Sundar Pichai reports that over 25% of all new code is now generated by AI. And Dario Amodei, CEO of Anthropic, recently told the Council on Foreign Relations that within a year, AI could be writing nearly all code produced globally.

This isn’t about ChatGPT. It’s about a new architecture for work. AI isn’t just helping; it’s enhancing large portions of white-collar, knowledge-based work, starting with IT services, but now moving into other sectors. 

• Salesforce paused technical hiring and laid off 1,000 people.
• Google launched a voluntary exit program for 20,000 workers, citing AI.
• DBS Bank (Singapore’s largest) announced plans to cut 4,000 roles, as agents now handle work previously done by humans.
• IT unemployment in the U.S. jumped from 3.9% to 5.7% in just one month (WSJ, Jan 2025).

The AI revolution isn’t coming. It’s here. And leaders need to understand how to start putting agents to work.

Agents are not some sort of mystical beings, and not all agents are built the same — how they work with you depends on what kind of ‘digital teammate’ they are.

Think of agents like brilliant silicon-based interns – tireless, fast, and a bit naïve – that need help with executive functions. Some need supervision nearly every step of the way. Others can run more complex workflows with minimal oversight.

The range of capability, autonomy, and responsibility is vast—and understanding a simple taxonomy can be helpful.

Task Agent: Handles specific, bounded tasks. E.g., generate test scripts.
Workflow Agent: Runs a full sequence of steps across tasks. E.g., ingest data, clean, validate, and publish.
Interface Agent: Engages with humans or systems to receive/give instructions. E.g., customer-facing assistant.
Governor Agent: Oversees, validates, coordinates other agents. E.g., ensures quality, trust, compliance.
You wouldn’t hire a junior analyst to run strategy. Same with agents — autonomy and purpose matter.

Let’s move from theory to execution. What does this actually look like in a messy, real-world enterprise?

A major bank ran into a wall. They were operating a 40-year-old legacy platform with 900,000 lines of code (much of it in PL/1 from the 1960s), handling 900 million transactions per year, with over 100 approval workflows.

Using an AI platform purpose-built to close the last mile from insight to execution, our company (Ascendion) reverse-engineered 700,000 lines of code in just 3 weeks, analyzed 4,200+ use cases, and created a 3-year roadmap. This led to real business impact. 

• Delivery at 1/3 the cost, 1/2 the time
• ~50% higher developer efficiency
• ~60% reduction in technical debt
• Modernized UX, alerts, and self-healing systems
• Secure, scalable architecture with zero business disruption

This is AI arbitrage – freeing up capital by shifting work from people to machines – in action.

It’s the early days of the true Digital Revolution – powered by AI. There are still a lot of questions, but a no-regrets path ahead for business and technology leaders is now clearing.

• Start with IT, but Don’t Stop There

Software development is the beachhead for AI-driven transformation, but the impact of agentic AI will rapidly extend into sales, marketing, customer support, finance, HR, and operations. Any business function built around knowledge work is within scope for automation and augmentation. Organizations that begin experimenting beyond IT today will gain a competitive advantage in productivity, scalability, and decision-making.

• Carbon + Silicon = The Future of Work

The future workplace will be built on collaboration between humans (carbon-based intelligence) and AI agents (silicon-based intelligence). Successful organizations will not simply use AI to reduce costs; they will redesign workflows so humans and intelligent agents complement each other. Leaders who embrace this partnership model will unlock higher productivity, faster innovation, and stronger business outcomes.

• Set New Metrics for Humans + Agents Building Software

Traditional software delivery metrics will evolve in an AI-enhanced development environment. Organizations should begin tracking new KPIs such as percentage of agent-written code, compliance automation rates, delivery acceleration, validation efficiency, and human-agent collaboration effectiveness. Metrics like 80%+ AI-generated code, 90%+ compliance accuracy, and significantly reduced time-to-delivery will become increasingly common benchmarks for modern engineering teams and technology partners.

• Redesign Your Technology Teams

The structure of technology teams will fundamentally change in the age of agentic AI. Instead of relying primarily on traditional software engineers, future-ready teams will include specialists focused on validation, AI training, prompt engineering, governance, workflow orchestration, and agent lifecycle management. High-performing AI-enabled organizations may operate with smaller engineering cores supported by larger AI operations and oversight functions.

• Build Your Ecosystem

No organization can fully implement agentic AI in isolation. Success will depend on building a strong ecosystem of AI model providers, integration partners, governance frameworks, automation platforms, security systems, and domain experts. Companies that invest early in strategic partnerships and collaborative ecosystems will be better positioned to scale AI capabilities securely and efficiently across the enterprise.

The signals are loud and clear. Wringing value from agent-powered work isn’t optional. It’s the new competitive capability. That means building systems, teams, and mindsets around augmented work.

It may be tempting to wait for the souffle to rise, for the technologies to harden and mature, for people to grow much more accustomed to working in new ways with silicon-based team-mates.

You could, but let history motivate you. We’re already passing the innovator phase and diving into the early adopter phase. Fast followers often outperform the pioneers (think: Microsoft after Netscape, Netflix after Blockbuster, Apple after Palm). But late-movers? They often fade.

In nearly every sector, the AI inflection point is here. You can pilot from the front — or get automated from the back. Leaders who embrace agentic AI now will unlock productivity, accelerate transformation, and leap ahead.