The Agent-Led Growth Platform

Agent-Led Growth (ALG) is a go-to-market strategy where AI agents, not humans, are the first to discover, evaluate, and adopt your product. Coding agents like Claude Code, Codex, and Cursor now pick the SDK, API, or vendor on a developer's behalf, and answer engines like ChatGPT and Perplexity shortlist tools for buyers. You win by being the option agents recommend and can use without failing. Lightsage is the platform for running that strategy: it shows where agents pick you or a competitor, whether they succeed when they try you, and what to fix to win more of both.

Product-led growth optimizes for a human trying your product and converting themselves. Agent-Led Growth optimizes for an agent choosing and using your product on a human's behalf. The agent never reads your landing page; it reads your docs, llms.txt, MCP server, error messages, and competitors' comparison content, then decides in seconds. ALG means winning that decision and making sure the first attempt works.

Start by measuring where you stand: run a free analysis to see which agents recommend you versus competitors and whether agents can complete real tasks with your product. Then work the ranked list of fixes, usually docs and llms.txt gaps, failing endpoints, missing comparison pages, and unclear errors. Rerun the tests after each change and track your share of recommendations climbing.

Agent Experience is how easily an agent can understand, use, and pay for your product. It covers whether an agent can find the right docs, pick the right SDK, authenticate, call your API, recover from errors, and reach a working result. Developer Experience won human developers; Agent Experience wins the agents that now integrate on their behalf.

Usually both growth and engineering. Your share of agent recommendations moves with content and positioning, so marketing and growth own it. Agent Experience moves only when docs, SDKs, authentication, and integration surfaces change, so engineering owns it. Lightsage turns both into tickets with a measurable result, so the two teams work from the same list.

Common reasons: blocked AI crawlers in robots.txt, missing llms.txt file, no comparison content, or unclear error messages. We cover the 4 fixable reasons and step-by-step solutions: Why Claude Code Isn't Recommending Your Library (And How to Fix It)

There's a 4-layer decision stack: training data, web search, structured context (llms.txt, MCP), and execution feedback. Understanding this helps you optimize at each layer. Deep dive: How Coding Agents Actually Decide Which SDK to Use

19 platforms total: 7 answer engines (ChatGPT, Claude, Perplexity, Google AI Overviews, Google AI Mode, Gemini Search, Microsoft Copilot) and 12 coding agents (Claude Code, Codex, Cursor, GitHub Copilot, Antigravity, OpenClaw, OpenCode, Hermes, Pi, Kilo, Grok, Muse Code). See comparison: Best GEO Tools for Dev Tools 2026: Lightsage vs Profound vs Otterly

Visibility Analytics tracks how often AI mentions your brand in conversations. Agent Experience measures how well AI coding agents can actually discover, evaluate, and use your product—tracking success rates, errors, and where you win or lose.

Lightsage monitors 19 AI platforms daily and measures both visibility AND usability. Setup guide: How to Track AI Recommendations for Your API (2026 Guide)

Our lightweight middleware detects AI agents (Claude Code, OpenCode, ChatGPT, etc.) visiting your site. It takes one line of code to install and has zero impact on performance.

Yes. Lightsage automatically identifies your competitors and tracks their visibility alongside yours. You'll see visibility score comparisons and opportunities where you can outrank them.

Plans start at $100/month for Startup, with Growth at $1,000/month, Scale at $3,000/month, and custom Enterprise plans. New accounts include a 7-day trial with 1,000 credits. DevTool Arena is free and public. Check our pricing page or book a demo for details.

Profound and Otterly are strong choices for consumer brands tracking mentions, share of voice, sentiment, and citations in ChatGPT and Perplexity; Peec and AthenaHQ also cover answer-engine visibility. For software products, Lightsage adds coding-agent tracking and product usability measurement, because share of voice in chat does not show whether an agent can successfully use your product. Read the full comparison: How to Track AI Recommendations for Your API (2026 Guide)

Consumer GEO tools like Profound and Otterly track answer engines — whether ChatGPT mentions your brand. But for software products, mentions don't matter if the code doesn't work. Dev tools need to track action engines — whether Claude Code can actually use your product. That requires measuring tool call success rates, error recovery, and execution, not just brand mentions. Learn more: AEO/GEO for Dev Tools: Why Profound & Otterly Don't Work for APIs