The next customer for a software company may not be a person scrolling through a pricing page. It may be an AI coding agent, asked by a developer to choose a database, install an SDK, connect an API, or fix a broken integration.
That shift is still early, but it is already changing how software is discovered and adopted. Tools such as Claude Code, Codex, Cursor, GitHub Copilot and OpenCode can now search for products, compare options, read documentation and write implementation code on behalf of users. In that world, a company’s website is no longer the only front door. Its documentation, APIs, SDKs, command-line tools and machine-readable interfaces become part of the sales funnel.
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Lightsage, a San Francisco-based startup founded by Singaporean founders Jun Liang Lee (CEO) and Sean Er (CTO), wants to build the analytics layer for this new behaviour. The company has raised US$4 million in funding led by Nexus Venture Partners to develop what it calls an Agent-Led Growth platform.
The round also includes operators from the developer tools and AI ecosystem, including former Salesforce CTO Steven Tamm, Postman CEO Abhinav Asthana, Apollo CEO Matt Curl, DocuSign President and GM of Growth Robert Chatwani, Resend CEO Zeno, Firecrawl co-founder Eric, Daytona CEO Ivan, Tinyfish COO Shuhao, and others.
From product-led to agent-led growth
For the past decade, many software companies have optimised around product-led growth, a model in which users discover, try and pay for products with limited involvement from sales teams. The playbook was built around human behaviour: search rankings, landing pages, onboarding flows, product analytics, emails and conversion funnels.
AI agents disrupt that pattern because they may compress discovery, evaluation and implementation into a single workflow. A developer might ask an agent to “add payments to this app” or “find the best OCR API for invoices”, and the agent could decide which vendor to use before the human ever sees a shortlist.
That matters for Southeast Asia, where many startups run lean engineering teams and rely heavily on global developer infrastructure. A fintech in Jakarta, a logistics startup in Ho Chi Minh City or a SaaS company in Singapore may increasingly use AI coding tools to speed up product work. If those agents default to familiar global vendors, newer or regional software companies could struggle to be discovered, even if their products are technically strong.
Lightsage’s core argument is that visibility in AI-generated answers is only one part of the problem. A product may be mentioned by an AI assistant, but still lose the “agent customer” if its documentation is confusing, its SDK fails, its authentication flow is unclear, or its API examples cannot be executed reliably.
“We are moving from an internet where AI tells people which software to use to one where AI increasingly uses the software itself,” said Lee. “Visibility still matters, but the real test is whether an agent can understand your product and get to a successful outcome.”
How the platform works
Lightsage allows software companies to see their product through an agent’s perspective. Its platform runs simulations across answer engines and coding agents, checking not only whether a company appears against competitors, but what happens after that.
Agents are assigned real tasks that require them to navigate documentation, pick the right tools and use APIs, SDKs, CLIs, Model Context Protocol servers and agent skills. MCP, an emerging standard popularised in the AI developer ecosystem, allows AI systems to connect more easily with external tools and data sources.
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When the agent fails, Lightsage identifies where the workflow broke. The issue could be poor discoverability, missing examples, inconsistent documentation, authentication problems, an API endpoint, an SDK implementation, or an incompatible MCP server. Teams can then fix the issue, rerun the test and measure whether the agent completes the task more successfully.
The company also tracks real agent traffic, showing when agents visit a customer’s website or documentation, what they interact with, and whether those journeys lead to product usage. Over time, Lightsage wants to feed those insights back into development and deployment workflows so products can continuously improve for both agents and humans.
The platform currently supports Claude Code, Codex, Cursor, GitHub Copilot, OpenCode and other coding agents.
Early users and a new analytics gap
Lightsage is beginning with developer software, where the behaviour of coding agents is easiest to observe. Its early customers include Firecrawl, Reducto, Daytona, Rime and Tinyfish. These companies use the platform to understand why agents recommend certain products, where integrations fail and whether changes to documentation or product flows improve outcomes.
A typical case may start with a company discovering that a coding agent keeps recommending a rival. Lightsage then recreates the same task across multiple products and agents to determine whether the problem is awareness, documentation quality, or the actual product experience.
This is an analytics gap that traditional software tools were not designed to handle. Human acquisition is usually tracked through search terms, ad clicks, referral links, sign-ups and sales calls. Agents may not follow those paths. They can discover a product through generated answers, inspect documentation directly, call an API and influence a purchase without producing the same trail of clicks.
Their behaviour is also unstable. Different coding agents can approach the same task differently, and their preferences may shift as models are updated. A workflow that works for one agent may fail in another.
Abhishek Sharma, partner at Nexus, framed this as a wider change in online commerce. “AI is now shifting that agency from humans to agents, which can discover, evaluate and act on a customer’s behalf,” he said. “Lightsage is building the intelligence infrastructure for this new era of autonomous browsing, helping companies optimise for agent conversion, not just awareness.”
Competitive landscape
Lightsage sits at the intersection of AI search optimisation, developer experience testing and product analytics. Its closest rivals are likely to come from several directions rather than one neat category. Generative engine optimisation startups such as Profound, AthenaHQ, Scrunch AI and Peec AI help brands understand how they appear in AI answers, while developer observability and AI infrastructure tools such as LangSmith, Helicone and Langfuse focus on monitoring AI applications and model behaviour.
Traditional product analytics companies, including Amplitude, Mixpanel and PostHog, already help software teams understand human users. Lightsage is betting that agent behaviour will become distinct enough to need its own system of record.
The challenge is whether “agent-led growth” becomes a durable software category or remains a feature added by existing analytics and developer tools platforms. Large incumbents already own parts of the workflow, from code assistants to API platforms and observability stacks. Lightsage will have to show that agent discovery, agent experience and agent attribution are not just interesting signals, but commercial levers that affect revenue.
Why it matters for Southeast Asia
For Southeast Asian startups, the rise of agent-led software adoption cuts both ways. On one hand, small teams can use AI agents to build faster, integrate complex tools and compete more effectively with better-funded rivals. On the other, if agents concentrate attention on a narrow set of familiar vendors, local or emerging software companies may find it harder to break into global workflows.
This could be especially relevant in areas where the region is producing more infrastructure and B2B software, from fintech APIs and compliance tools to logistics software and vertical SaaS. Winning a human developer’s trust may no longer be enough. Products will also need to be legible to machines.
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Lightsage plans to use the new funding to expand its agent evaluation, analytics, attribution and optimisation capabilities across APIs, SDKs, CLIs, MCP servers and agent skills. Developer tools are the starting point, but the company expects agent behaviour to spread into B2B software, infrastructure and payments.
If that happens, the old growth funnel may not disappear, but it will have a new participant. The buyer may still be human. The first user may increasingly be an agent.
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