Our Agentic Future
AI Agents are coming faster than you think, but that is a good thing
You will have a co-worker soon who is an AI agent, not a robot but an agent. The difference is that an agent can think for itself. It can reason, analyse and pivot. It can perform tasks by accessing systems and other agents. In short, it is your smart, task-orientated co-worker that we all need. This future is already here.
The Evolution of Enterprise Software
The enterprise software journey can be broken down into three distinct waves:
Wave 1: The Cloud Migration
Remember the days of Oracle databases, where data access was limited and structures were rigid? The first wave of innovation involved moving these systems to the cloud, making data dynamic and accessible. It was similar to how bookkeepers transitioned from paper ledgers to Excel spreadsheets – a fundamental shift in how we stored and accessed information that led to an explosion of workers in the finance field.
Wave 2: The SaaS Revolution
The second wave brought us Software-as-a-Service (SaaS). These platforms excelled at two things: ingesting company data and providing intuitive interfaces to access, filter, sort, and manipulate that data. This wave answered the "WHAT" and "WHO" questions of business operations – what is my company doing, and who are we doing it with?
Wave 3: The Agentic AI Future
Now, we're entering the third wave: agentic AI. This wave promises to answer and execute the "HOW." Your cloud-based data isn't just a repository anymore – it's becoming an actionable knowledge bank that AI agents can interpret and act upon.
The True Value Proposition
The most exciting aspect of this transition is the shift is that software will bifocate. Software will be easier to create and deploy. So “software creation” costs will fall. Software will be as easy as prompting. However, those companies that own data, that are the knowledge bank of an enterprise will morph into being the central nervous system of a company. Here they will (rightly) compare that cost of the system to outcomes, not processing power. Consider sales and customer service departments, traditionally the highest-value verticals in any organization. Soon, instead of comparing software based on monthly subscription costs, companies will evaluate AI systems against their best human performers. The question won't be "How much does the software cost?" but rather "How does its performance compare to our top sales representative?"
The Interoperability Revolution
Perhaps the most profound impact of agentic AI will be in how it reshapes software interoperability. Traditional API-based integration solved deterministic interactions between systems. But AI agents introduce a new paradigm of non-deterministic coordination.
Imagine this scenario: A customer inquiry about flight status doesn't just trigger a database query. Instead, an AI agent:
Accesses Salesforce to pull customer records
Checks real-time flight data in the Global Distribution System
Reviews enterprise sales contracts
Analyzes previous interaction transcripts
Synthesizes all this information into a coherent, contextual response
What's remarkable is that this happens without pre-built integrations. Tools like OpenAI's Operator allow agents to navigate interfaces visually, unlocking a long tail of capabilities that developers never had time to build traditional integrations for.
The Promise of Infinite Scale
Think about all the tasks your organization would tackle if you had a thousand PhD-level interns at your disposal. That's the promise of agentic AI. These agents can:
Navigate multiple software systems simultaneously
Collect and analyze data from diverse sources
Execute complex workflows across platform boundaries
Make informed decisions based on comprehensive data analysis
Open Questions and Challenges
As with any paradigm shift, the rise of agentic AI raises important questions:
Coordination Models: Will agent interactions follow a hierarchical model with clear leadership, or will we see more peer-to-peer collaboration?
Agent-Specific Infrastructure: Will we need specialized platforms designed specifically for agent-to-agent interaction?
Access Management: How do we handle permissions and security when agents need to access multiple systems?
Economic Models: What business models will emerge in an ecosystem where AI agents perform complex cross-system tasks?
Quality Control: How do we prevent error accumulation and ensure reliable outputs when multiple agents collaborate?
Embracing the Future
The transition to agentic AI isn't just another technology trend – it's a fundamental reimagining of how enterprise software works. We're moving from a world of rigid integrations and human-driven processes to one where AI agents can navigate, understand, and act upon our digital infrastructure with unprecedented flexibility.
For businesses, the message is clear: your next coworker might not have a desk, but they'll have access to every system in your organization and the intelligence to use them effectively. The future of work isn't just about humans using AI tools – it's about humans and AI agents collaborating as peers in a digital ecosystem.
The question isn't whether this transition will happen, but how quickly organizations will adapt to and benefit from this new paradigm. Those who embrace and prepare for this future will find themselves with a significant competitive advantage in the age of agentic AI.