The Rise of AI Agents: What Founders and Builders Need to Know

Why autonomous AI agents are more than hype, and how to think strategically about them.

Jul 16, 2025

Not long ago, the idea of giving an AI system a task and having it complete that task, without constant human direction, sounded like science fiction. Today, it’s fast becoming reality.

From OpenAI’s Assistants API and Anthropic’s Claude 3.5 to early experiments like Devin (Cognition’s AI software engineer), we’re watching the rise of autonomous agents, AI systems that can take initiative, call tools, and complete multi-step tasks on your behalf.

The implications are massive. For founders, builders, and operators alike, the question is no longer if agents will change how we build products and run businesses, but how to harness them responsibly and effectively.

Let’s break it down.

What Is an AI Agent, Really?

Forget the buzzwords. Here’s a practical definition:

An AI agent is a goal-oriented system that can take actions, use tools, and make decisions with limited human input.

Unlike chatbots that wait for you to prompt them, agents are active participants in the conversation. You can give an agent a goal: “Find the top three CRM platforms for SaaS startups and compare them,” and it will plan, browse the web, gather data, and return results, possibly even formatting them in a table or emailing them to you.

A modern agent stack typically includes:

A foundation model (LLM) for reasoning and language
Memory for context across steps
A planner to break down goals into actions
Tool use: APIs, browsers, or other software integrations
Feedback mechanisms to learn from results or corrections

They aren’t sentient. They’re not general AI. But they are capable of executing complex workflows when designed well.

Why Now? What’s Driving the Surge in AI Agents?

There’s a convergence of factors accelerating agent development:

More capable LLMs: Models like GPT-4o and Claude 3.5 now handle complex reasoning, multi-turn conversations, and tool use reliably.
Expanded tool ecosystems: OpenAI’s Assistants API, LangChain, and AutoGen make it easier to build structured, multi-tool workflows.
Persistent memory: Agents can now store and recall information across sessions, allowing continuity.
Productivity demand: Enterprises and startups alike are hungry for automation that goes beyond task-specific copilots.

VC funding reflects the trend:

Cognition raised $21M to build AI agents like Devin, an autonomous coding assistant.
Reka AI, Imbue, and Adept are all betting on agent-first models.
Dozens of open-source frameworks (e.g., SuperAGI, CrewAI) have emerged in just the past year.

We’re moving from command-line prompting to dynamic, delegated intelligence.

What’s the Catch?

Despite the promise, AI agents today are far from plug-and-play. Most fail quietly or unpredictably. Common limitations include:

Fragile planning: Agents often hallucinate steps or loop endlessly.
Poor observability: Hard to debug or intervene mid-task.
Latency + cost: Multi-step reasoning chains require significant compute and time.
Lack of safety: Tool use can go wrong (e.g., deleting files, sending incorrect messages).
Weak metrics: Success is hard to define—was the task completed well, or just completed?

These systems don’t generalize well. They require narrow, structured domains to succeed reliably.

Where AI Agents Are Actually Working Today

While the dream of fully autonomous “do-anything” agents is still out of reach, real-world traction is happening in niche but high-value areas:

Internal Operations

Sales and RevOps agents: Automating lead routing, CRM data entry, pipeline analysis.
Customer support triage: Agents classify tickets, draft responses, or escalate intelligently.

Developer Productivity

AI coding agents like Devin can write, test, and debug code semi-autonomously within a defined repo and tooling environment.

Knowledge Work

Research agents scrape, summarize, and format data from multiple sources.
Meeting assistants automatically schedule, follow up, and generate action items.

Consumer Utility (Still Immature)

Tools like MultiOn and Rabbit R1 aim to execute online tasks—such as booking tickets and scheduling meetings—but the results are mixed. Most still require human oversight to work properly.

Should You Build an Agent? Strategic Filters for Founders

Before you jump into the agent race, ask:

Is the task repeatable and well-defined?
Agents thrive in structured environments—e.g., reconciling transactions, pulling dashboards—not open-ended creative tasks.
Do you control the environment?
Agents break easily when APIs change or workflows lack guardrails. The tighter the system, the better they perform.
Is autonomy a feature or a liability?
For high-stakes workflows (finance, health, legal), full autonomy may be too risky today. Opt for supervised agents or copilot hybrids.
Can you monitor and course-correct?
Successful deployments include approval loops and observability layers. Autonomy should be earned, not assumed.

Only pursue high-feasibility, high-value use cases with true agent architecture. Everything else can likely be solved more simply.

The UX of AI Agents Is Underrated

Technical capability doesn’t guarantee usability. Many early agents fail not due to weak models, but because users don’t know what’s happening.

Best practices:

Let users see and edit the agent’s plan.
Enable “pause and approve” checkpoints.
Provide clear logs and controls.
Design for trust recovery—what happens when the agent gets it wrong?

Think of your agent more like a junior team member than a black box.

What’s Next: AgentOS and the Platform Race

The ecosystem is coalescing around broader ambitions:

Multi-agent systems: Agents that collaborate, delegate, or even “hire” other agents.
Memory-first infrastructure: Persistent agents that learn and adapt over time.
Vertical agent platforms: Think legal, biotech, logistics. Domain-specific agents with access to specialized tools and data.
Agent Operating Systems: Frameworks like LangChain, CrewAI, and OpenAI’s Assistants API are racing to be the default stack for agent orchestration.

We’re in the “browser wars” era of AI agents, and the stack is far from settled.