Andrew Lee

Tasklet’s core bet is that model-led agents will overtake deterministic workflows because agents can route around the real-world exceptions that break flowcharts. Andrew Lee concedes that today’s models are “probably” somewhat less reliable for many business applications, but predicts that gap disappears within six months and eventually reverses: “Rather than having software wrapping LLMs, you have LLMs wrapping software.”

The product is evolving from recurring automation into a persistent, language-native virtual employee. A high-level agent retains responsibilities and feedback while sub-agents execute individual runs; users can also return for ad hoc work, and most messages already fall into that category even though nearly every paying user has an automation. Some customers name agents, give them dedicated email accounts, and treat them as colleagues: “This is Joe, my EA.”

Lee still selects models “on vibes,” with Anthropic decisively preferred for long tool-using sequences despite GPT-5’s stronger published task-length result and lower price. His claim is not that Sonnet always gives the best first answer, but that small advantages compound across 100 iterations; the fact that GPT-5 costs less than half as much without triggering broad switching is his market-based evidence that “the real-world utility” of Sonnet is higher.

Tasklet’s distribution wedge is universal connectivity rather than a curated handful of integrations. It combines 3,000-plus integrations and business tools, arbitrary APIs, MCP servers, and computer use; one paying customer even replaced Notion’s official MCP with Tasklet’s model-generated direct API connection because it worked better. Lee’s emerging MCP view is blunt: if tools merely mirror documented endpoints and models can discover those endpoints themselves, “what’s the point of MCP?”

Long-lived agents make context engineering and compute infrastructure—not the basic agent loop—the consequential technical work. Tasklet replaced an always-present mutable JSON memory that became unwieldy after a week or two with an agent-managed SQL database, while pursuing compaction, selective retrieval, and eventual “uncompacting” of old history. The north star is an economically impossible but experientially valuable illusion: “one big long chat” in which everything remains available and intelligently considered.

The economics are early-stage and unfavorable, but Lee sees familiar cost curves and a near-term Haiku 4.5 lever. Shortwave progressed from launches that might have bankrupted the company to healthy, though sub-90%, margins; Tasklet is currently “strongly margin negative,” consumes far more tokens, and must police zombie automations among free users. Haiku 4.5 costs roughly one-third as much as Sonnet, potentially allowing much larger quotas without equivalent margin damage.

The strategic endgame is a trusted horizontal agent platform, but Lee believes speed is the only present moat. He expects general-purpose models and generated interfaces to eliminate much vertical SaaS—eventually even Shortwave in its current form—while direct APIs and computer use erase connector libraries accumulated over years. Tasklet’s launch was adding revenue much faster per unit time than Shortwave ever had, yet the durable enterprise opportunity depends on becoming “the most trusted way” to deploy agents, with permissions, auditability, compliance, and potentially insurance.