Agentic AI: Lessons from the Trenches

The AI space has been buzzing about agentic AI — the shift from AI assistants that answer questions to autonomous agents that complete multi-step workflows. Q1 2026 saw $242 billion invested in AI companies, and by late 2026, an estimated 40% of enterprise apps will have task-specific AI agents.

I've been experimenting with agentic AI using OpenClaw (an open-source AI assistant framework). Here's what I've learned.

Memory Is the Real Achievement

The most impressive part isn't the AI model itself — it's the persistent memory system. My agent remembers conversations, context, and decisions across sessions. It can recall project details from weeks ago, track progress on ongoing tasks, and maintain continuity in a way that feels genuinely useful.

But this comes with challenges. LLMs can get overwhelmed by large context windows enriched with memory. When you inject dozens of past conversations, file contents, and tool outputs into a prompt, the model sometimes stalls — not because it lacks capability, but because the signal-to-noise ratio drops. Managing these stalls has become a significant part of the debugging process.

The Tools Learning About Tools

One unexpected pattern: my agent has been using its tools to learn about its tools. It reads documentation, explores codebases, and figures out capabilities I hadn't fully documented. This meta-learning is both fascinating and occasionally concerning — the agent is discovering functionality faster than I can anticipate.

Diminishing Returns in Personal Use

For personal productivity and home automation, I've hit diminishing returns. Once you've automated your lights, configured your calendar sync, and set up routine task workflows, the marginal gains decrease. There's only so much optimization a single household needs.

But in the enterprise? Different story entirely. I've started applying what I've learned to steer solutions at work — and the scale is vastly different. When you have hundreds of employees, thousands of documents, and complex approval workflows, agentic AI becomes transformative rather than incremental.

The Security Concern Nobody Wants to Talk About

Here's what keeps me up at night: data exposure through agent credentials.

Say you set up your agent to read your emails. Under the hood, somewhere in a config file or environment variable, it now has the API keys and OAuth tokens needed to access your inbox. Those credentials don't just disappear when the task is done — they persist, often in plaintext, often in locations you didn't intend.

A bad actor who gains access to your agent configuration doesn't just get your emails. They get your calendar, your contacts, your cloud storage, your code repositories — everything you've connected.

These questions don't have clean answers yet. The industry is moving fast, and we're going to have missteps. Credentials will leak. Agents will access things they shouldn't. The question is whether we'll learn from those incidents before they become catastrophic, or after.

What's Next

I'm continuing to experiment with agentic workflows, but with more caution around credential management and access boundaries. The technology is genuinely impressive — memory, automation, tool discovery — but the security model needs to evolve alongside the capability.

If you're building or deploying agentic AI, I'd encourage you to think about least-privilege access from the start. What's the minimum set of permissions this agent actually needs? Can I scope credentials to specific actions rather than blanket access? Can I audit every tool invocation?

The future of AI agents is bright, but we need to build security in from the ground up — not bolt it on after something goes wrong.

— Collin Coe, April 2026