How should businesses think about AI agents? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Answer by Chris Taylor, CEO at Fractional AI, on Quora:
AI agents are everywhere. Companies are racing to build them, investors are pouring in funding, and thought leaders are proclaiming the dawn of a new era where agents replace human workers.
The problem? AI agents are already delivering real value, but the industry is also experiencing ‘agent-washing’ – companies overusing the term for branding without delivering true agentic impact.
The Agentic Reality: A Spectrum, Not a Silver Bullet
Despite the excitement, agents joining the workforce as independent employees remains largely aspirational. However, AI-driven workflows and agentic systems are already delivering remarkable value within organizations, autonomously handling complex tasks like code generation and end-to-end customer support
The industry still hasn’t settled on a single definition of what makes an AI system an agent, and that’s OK. It’s helpful to think of AI agents on a spectrum, rather than a binary. Some AI-powered tools branded as agents focus on simple automation (handling repetitive tasks like sorting emails or answering common customer questions). Others are tool-using AI systems, where models connect with APIs and external tools to complete multi-step processes. Something becomes more ‘agent-like’ as it takes on greater decision-making responsibility, applies judgment, or integrates external tools.
Where Agents Actually Deliver Value
Rather than questioning whether AI agents are real, businesses considering putting agentic workflows in to action should focus on where they’ve already made an impact. An example of this is in a system designed to automate consulting-style market research interviews. Instead of relying on a single agent, multiple agents work together with specific roles: a voice agent conducts the interview, another recommends the next questions to ask, and another refines the written transcript for clarity. These agents work in tandem to autonomously manage the interview process end-to-end, ensuring a structured process without requiring human oversight.
Agents also provide value in several other areas:
- Customer Support Automation – AI chatbots handle repetitive customer inquiries (like checking on the status of a prescription or whether a flight will be on-time), with humans stepping in for complex cases.
- AI-Powered Interview Assistants – AI follows structured workflows to guide job interviews and assess responses.
- AI-Driven Talent Sourcing – AI agents work together to manage potential job candidates through the talent funnel. One scans resumes and online profiles to find the best matches, another personalizes outreach messages, and yet another schedules interviews, automating the recruitment pipeline while ensuring a human recruiter remains in the loop for final decisions.
The Pitfalls of Over-Engineering Autonomy
One of the biggest mistakes companies make is assuming more autonomy is always better. Sometimes, a simpler, deterministic workflow is more effective. Rather than aiming for complete autonomy, businesses should instead be asking “edihow much autonomy is needed to achieve our goals?” . The more independent an AI system is, the less predictable and reliable it often becomes. Rather than aiming for complete autonomy, AI should be designed to streamline efficiencies, automate repetitive tasks, and augment human decision-making.
Take Devin, the AI software engineer developed by Cognition. Marketed as the first AI capable of independently completing software development tasks, Devin wowed audiences with its ability to debug, deploy applications, and manage GitHub issues. But in reality? It still needs a lot of human oversight. While Devin is an impressive tool, it functions more like an advanced coding assistant than a fully autonomous coworker. Like most AI solutions today, it’s most effective when working alongside humans, not replacing them.
Instead of viewing agents as a miracle solution for every problem, businesses should start by building AI that actually delivers value.
The Bottom Line: Winning with AI Requires Practical, Not Perfect, Autonomy
The hype around agents isn’t a bad thing. In fact, it’s helping businesses rally around AI adoption. But companies need to walk before they run. Full autonomy may feel like the ideal end state, but AI solutions that solve real problems effectively will win the day every time. When scoping out your AI agent project, ride the hype wave to secure internal support and resources, but recognize that true autonomy isn’t always the right answer. Want to unlock AI’s full potential in your business? Start with simple, impactful automation—and build from there.
This question originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world.
