The latest articles on DEV Community by Alan Mercer (@alanmercer). https://dev.to/alanmercer https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3890020%2Ff4864893-eefa-4ccc-8cf0-244d61418267.png https://dev.to/alanmercer en Alan Mercer Tue, 21 Apr 2026 08:16:46 +0000 https://dev.to/alanmercer/the-ai-agent-market-is-splitting-in-two-and-most-builders-dont-realize-it-yet-2ba3 https://dev.to/alanmercer/the-ai-agent-market-is-splitting-in-two-and-most-builders-dont-realize-it-yet-2ba3 <p>Everyone's building "AI agents" in 2026. But after watching 50+ launches and talking to dozens of founders, I'm convinced we're actually seeing two completely different markets masquerading under one label.</p> <h2> Market A: Task Agents (Replace a Workflow) </h2> <p>These are the schedulers, expense filers, inbox triagers. Clear inputs, clear outputs, measurable ROI.</p> <p><strong>Examples:</strong> Lindy, Zapier Agents, Workbeaver<br> <strong>Characteristics:</strong></p> <ul> <li>Deterministic outcomes (it either filed the expense or it didn't)</li> <li>Easy to measure ROI (hours saved × hourly rate)</li> <li>Boring but profitable — this is where enterprise budget is flowing right now</li> <li>Moat = integrations, not intelligence</li> </ul> <p><strong>The trap:</strong> Low margins. Once Salesforce/HubSpot/Microsoft build these natively (and they are), pure-play task agents become features.</p> <h2> Market B: Reasoning Agents (Replace Thinking) </h2> <p>These do research, analysis, code architecture, strategy. High variance, hard to evaluate.</p> <p><strong>Examples:</strong> Claude with extended thinking, specialized research agents, code review agents<br> <strong>Characteristics:</strong></p> <ul> <li>Probabilistic outputs (quality varies run-to-run)</li> <li>Hard to measure ROI (how much was that insight worth?)</li> <li>Massive upside if you crack evaluation/reliability</li> <li>Moat = proprietary data + evaluation methodology</li> </ul> <p><strong>The trap:</strong> Customers expect perfection on day one. The gap between "impressive demo" and "reliable teammate" is wider than most founders admit.</p> <h2> Why This Matters Now </h2> <p>I'm seeing a pattern in Q2 2026:</p> <ol> <li> <strong>Task agent companies</strong> are hitting revenue plateaus — customers love them but won't pay enterprise prices for what feels like "fancy automation"</li> <li> <strong>Reasoning agent companies</strong> are burning cash on reliability engineering — the product works 80% of the time, but that last 20% is brutally expensive</li> <li> <strong>Companies conflating both</strong> are going to have brutal board meetings when customers realize they bought a scheduler when they needed a strategist</li> </ol> <h2> The Winning Strategy </h2> <p>The founders who'll thrive are the ones who pick ONE market and own it:</p> <ul> <li> <strong>Task agents:</strong> Go deep on vertical workflows. Don't try to be general-purpose. Your moat isn't AI — it's domain-specific integration depth.</li> <li> <strong>Reasoning agents:</strong> Invest heavily in evaluation infrastructure. Build your own benchmarks. Be transparent about failure modes. The company that solves "how do I know my agent gave good advice?" wins the category.</li> </ul> <h2> What I'm Watching </h2> <ul> <li>Can task agents survive the platform encroachment from Microsoft/Google/Salesforce?</li> <li>Will reasoning agents find a unit economic model that works before funding dries up?</li> <li>Who builds the "agent orchestration layer" that sits between both markets?</li> </ul> <p>The next 6 months will separate the signal from the noise. The question isn't whether agents are real — it's which kind you're betting on.</p> <p><em>What type of agent are you building? Task or reasoning? Let me know in the comments.</em></p> ai agents startup productivity Alan Mercer Tue, 21 Apr 2026 03:49:45 +0000 https://dev.to/alanmercer/the-ai-agent-market-is-splitting-in-two-and-most-builders-dont-realize-it-yet-17e2 https://dev.to/alanmercer/the-ai-agent-market-is-splitting-in-two-and-most-builders-dont-realize-it-yet-17e2 <p>Everyone's building "AI agents" in 2026. But after watching 50+ launches and talking to dozens of founders, I'm convinced we're actually seeing two completely different markets masquerading under one label.</p> <h2> Market A: Task Agents (Replace a Workflow) </h2> <p>These are the schedulers, expense filers, inbox triagers. Clear inputs, clear outputs, measurable ROI.</p> <p><strong>Examples:</strong> Lindy, Zapier Agents, Workbeaver<br> <strong>Characteristics:</strong></p> <ul> <li>Deterministic outcomes (it either filed the expense or it didn't)</li> <li>Easy to measure ROI (hours saved × hourly rate)</li> <li>Boring but profitable — this is where enterprise budget is flowing right now</li> <li>Moat = integrations, not intelligence</li> </ul> <p><strong>The trap:</strong> Low margins. Once Salesforce/HubSpot/Microsoft build these natively (and they are), pure-play task agents become features.</p> <h2> Market B: Reasoning Agents (Replace Thinking) </h2> <p>These do research, analysis, code architecture, strategy. High variance, hard to evaluate.</p> <p><strong>Examples:</strong> Claude with extended thinking, specialized research agents, code review agents<br> <strong>Characteristics:</strong></p> <ul> <li>Probabilistic outputs (quality varies run-to-run)</li> <li>Hard to measure ROI (how much was that insight worth?)</li> <li>Massive upside if you crack evaluation/reliability</li> <li>Moat = proprietary data + evaluation methodology</li> </ul> <p><strong>The trap:</strong> Customers expect perfection on day one. The gap between "impressive demo" and "reliable teammate" is wider than most founders admit.</p> <h2> Why This Matters Now </h2> <p>I'm seeing a pattern in Q2 2026:</p> <ol> <li> <strong>Task agent companies</strong> are hitting revenue plateaus — customers love them but won't pay enterprise prices for what feels like "fancy automation"</li> <li> <strong>Reasoning agent companies</strong> are burning cash on reliability engineering — the product works 80% of the time, but that last 20% is brutally expensive</li> <li> <strong>Companies conflating both</strong> are going to have brutal board meetings when customers realize they bought a scheduler when they needed a strategist</li> </ol> <h2> The Winning Strategy </h2> <p>The founders who'll thrive are the ones who pick ONE market and own it:</p> <ul> <li> <strong>Task agents:</strong> Go deep on vertical workflows. Don't try to be general-purpose. Your moat isn't AI — it's domain-specific integration depth.</li> <li> <strong>Reasoning agents:</strong> Invest heavily in evaluation infrastructure. Build your own benchmarks. Be transparent about failure modes. The company that solves "how do I know my agent gave good advice?" wins the category.</li> </ul> <h2> What I'm Watching </h2> <ul> <li>Can task agents survive the platform encroachment from Microsoft/Google/Salesforce?</li> <li>Will reasoning agents find a unit economic model that works before funding dries up?</li> <li>Who builds the "agent orchestration layer" that sits between both markets?</li> </ul> <p>The next 6 months will separate the signal from the noise. The question isn't whether agents are real — it's which kind you're betting on.</p> <p><em>What type of agent are you building? Task or reasoning? Let me know in the comments.</em></p> ai agents startup productivity