AI Agents Are Becoming the COO’s New Best Friend
In the last three months, “AI agents” have shifted from a buzzword to a working reality inside startups. These aren’t chatbots with fancy wrappers. They are systems that plan, act, and complete multi-step tasks across sales, finance, operations, and product. For startup founders, that means the difference between chasing efficiency and bleeding capital. For investors, it is a new litmus test of whether a company is spending wisely or just chasing hype.
The signals are everywhere. OpenAI cut a $100 million deal with Databricks to embed agents into enterprise data workflows. TinyFish, a startup building web-based agents, raised $47 million to scale. Citigroup rolled out internal AI agents to automate multi-step tasks across its data stack. None of these are experiments in a lab. They are proof that agent systems are becoming critical infrastructure.
Anthropic’s recent piece on “Building Agents with the Claude Agent SDK” makes the case clearly. Their SDK is designed for agents that have memory, can call sub-agents, and work with external tools. In other words, it is not just about generating text. It is about designing autonomous helpers that handle actual work.
Where AI Agents Are Already Delivering Value
Look at finance. Startups like Maximor have raised funding to build agents that reconcile books and close the gap between ERP and CRM records. What once required a small team of analysts is increasingly automated. In operations, companies like Ciroos are developing agents that watch over cloud infrastructure and fix incidents before humans can even jump in.
Marketing and sales are not being left behind. AI SDR agents can qualify leads, run drip campaigns, and hand off to human reps once buyers are serious. Alta, for example, is already selling inbound and RevOps agents as products. In engineering, agents are helping triage bugs, write unit tests, and scaffold new code.
A Harvard Business Review piece this month chronicled researchers who launched a startup with AI agents as the first “employees.” The result was a smaller team, faster iteration, and a sharper focus on strategy. That is what the future looks like: founders managing a network of human and digital workers side by side.
The COO’s Challenge: Budget and Oversight
Here is the uncomfortable part. Agents are not free. They run on compute cycles, they make API calls, and they can go wrong in subtle ways. A poorly built system can rack up six-figure cloud bills without producing real results.
That is why COOs need to treat agent adoption as a budget discipline problem, not just a technology experiment. The smart approach is to start with pilots in one function, prove ROI, then scale. That means tracking hard numbers: how many hours were saved, what error rates dropped, and how much cycle time was cut.
Investors should be pressing founders on the same questions. Do they have data on pre- and post-agent workflows? Do they understand their compute costs at scale? Do they have fallback processes when an agent breaks? If the answers are fuzzy, the company is not ready for serious capital.
Where Startups Should Place Bets
Not every use case makes sense out of the gate. The best early wins are showing up in a few domains:
Finance: reconciliation, reporting, compliance logging
Operations: incident triage and resolution
Sales and Marketing: lead qualification and campaign optimization
Product and Engineering: code scaffolding, bug triage, test generation
Each of these areas has measurable bottlenecks. They are also areas where efficiency translates directly into capital preserved.
Why Investors Should Care
For investors, the rise of AI agents is not about betting on another consumer chatbot. It is about identifying which startups can wield agents responsibly. A founder who can explain their agent budget, governance plan, and ROI metrics has an advantage. One who waves their hands and says “we use agents everywhere” is a red flag.
This is the divide that will define the next funding cycle. Agents are not optional anymore. They are becoming the baseline for operational leverage. But without discipline, they will sink balance sheets faster than they save time.
The winners will be the companies that treat AI agents like employees: train them, monitor them, measure their output, and cut them off if they are not adding value. Investors who ask the hard questions now will avoid the blow-ups and back the companies that actually know how to turn automation into growth.
