When agents replace clicks: stress-testing your software moats

Less than three months ago, Zimt mapped moats for B2B SaaS in 2026. Six moats. Six defensibility patterns for the AI era. But here's what we missed: those moats were designed for human users.

Your customer doesn't navigate your dashboard anymore. They prompt an agent. The agent calls your API, retrieves data, completes the task – and moves on. No sticky workflows. No muscle memory. No interface lock-in.

This is the agent stress test. And most software moats fail it.

The canary in the enterprise

Watch the Cursor vs. Claude Code battle. Cursor built a product around workflow – IDE integration, tab completion, the rhythm of how developers write code. Claude Code skips all of it. It takes a task, executes it, and returns the output.

Developers are choosing execution over workflow.

The numbers are stark. Anthropic's Claude Code reached 150,000 paying users within three weeks of launch – with zero workflow stickiness. Cursor, despite its 18-month head start on developer experience, faces users who increasingly want outcomes, not interfaces.

This isn't an anomaly. It's the pattern.

The seven moats, stress-tested

Here's the uncomfortable truth: the software industry built moats for humans to click through. When agents click instead, seven moats fall into three categories.

Moats that erode

1. Workflow. Your carefully designed user journey? Agents don't take journeys. They call endpoints. The 20%+ retention premium from deep workflow integration evaporates when the "user" is an API request. Workflow integration still matters – but only if you're orchestrating agents, not humans.

2. Integrations. Technical glue between systems was defensible because it was hard to build and harder to maintain. AI agents commoditize this overnight. An agent with API documentation can replicate your 40-integration moat in hours. The 4+ integrations that once drove retention become table stakes.

3. Data repository. Static data stores – your "single source of truth" – lose value when they lack context. Agents don't need your warehouse if they can query the source directly. Raw data without interpretation is a liability, not an asset.

Moats that shift

4. System of action. Products directly touching money, customers, and real-world outcomes still carry weight. An agent can analyze your CRM, but initiating a wire transfer or signing a contract still requires trust anchored in your system. The moat moves from data custody to action authority.

Moats that strengthen

5. Full context. Here's the new defensibility: rich understanding of users, tasks, and situations. Your proprietary data flywheel evolves from "workflow intelligence" to "contextual intelligence." The agent that knows your customer's previous decisions, constraints, and preferences outperforms the agent starting cold.

Research from Princeton's NLP group shows that AI agents with access to interaction history complete tasks with 34% fewer errors than those without prior context. Context compounds.

6. Brand. Trust persists across technology shifts. Enterprise buyers choosing between equivalent AI tools default to the vendor they trust. The trust and governance moat we mapped becomes more valuable, not less – because agentic systems require deeper trust to grant action authority.

7. Network effects. Data and trust-based networks strengthen with agents. Every agent interaction in your platform improves the collective intelligence available to all users. Your PLG flywheel accelerates – self-serve adoption spins the context moat faster.

How the original six moats adapt

The pattern: moats anchored to human behavior weaken. Moats anchored to data, trust, and outcomes strengthen.

The convergence thesis

Here's where it gets interesting. Several operators in this space argue that "full context" and "network effects" collapse into one defensibility layer: whoever owns the interaction graph wins.

The logic: context is only valuable if it's comprehensive. Comprehensive context requires network-scale data. Network-scale data requires network effects to accumulate. They become the same moat.

If this thesis holds, the endgame isn't "build context" but "build the network that generates context." That's a different strategic race.

The Brcic research paper calls this "Network Effect 2.0" – where value scales not with user count but with depth of personalized memory. The switching cost isn't your features. It's the agent's understanding of the user that lives inside your system.

The process power counterargument

One perspective worth taking seriously: the real moat is speed.

Hamilton Helmer's "process power" framework suggests that sustainable advantage comes from operational velocity – the ability to keep building faster than competitors can copy. In a market where AI capabilities shift every 12-18 months, the moat isn't what you've built. It's how fast you can rebuild.

This matters most at mid-market and enterprise layers. Those customers aren't swapping CRM every year. They'll back whoever they trust to keep pace with evolution and keep shipping.

If process power is the real moat, then the seven moats above become temporary advantages. The only enduring advantage is organizational speed.

Diagnostic signals

How do you know if your moat survives the agent stress test?

• More than 60% of your retention is explained by workflow habits rather than outcome value

• Your primary defensibility story involves "integrations" or "ecosystem"

• Your data advantage depends on users manually inputting information

• You can't articulate what happens when an agent completes the task instead of a human

• Your product requires a dashboard visit to deliver core value

If three or more of these describe your situation, your moat is eroding.

What to build now

1. Shift from workflow capture to context capture. Every interaction should accumulate understanding – not of what users clicked, but why they decided, what constraints they operate under, and what outcomes they achieved.

2. Build for agent-as-user. Design your API surface assuming the primary caller is an autonomous agent, not a human. This means structured outputs, clear action authorities, and audit trails.

3. Invest in trust infrastructure. Agents need permission to act. The vendor with the strongest governance story wins the action authority. SOC 2 was table stakes for data access. Agent-era trust means provable decision-making frameworks.

4. Accelerate the context flywheel. Self-serve isn't just about conversion anymore. Every free user generates context that improves your agents' performance for paid users. PLG becomes context-generating infrastructure.

5. Pursue network density over network breadth. In the agent era, a smaller network with deeper context beats a larger network with shallow data. Prioritize interaction depth per user over user count.

The uncomfortable conclusion

The moat map we published three months ago isn't wrong. It's incomplete.

Those six moats assumed humans as the primary interface. That assumption is now a liability. The software that wins the agent era will look different: less dashboard, more API. Less workflow stickiness, more contextual intelligence. Less user count, more interaction depth.

The gap between human-era moats and agent-era moats isn't a threat. It's the biggest untapped opportunity in enterprise software right now.

Different interface. Different moat.

Your homework: Map your current retention drivers. For each one, write what happens when an agent completes that task instead of a human. If your moat disappears in that scenario, you have 12-18 months to build something that survives the stress test.