Emergent, a Bengaluru-based startup known for its vibe-coding platform, has launched Wingman, a messaging-first autonomous AI agent designed to execute tasks across everyday tools. Announced by TechCrunch on April 15, 2026, the product expands the company from software creation into task execution: using chat to assign and monitor work, while the agent runs in the background to carry out routine actions across connected systems.
Wingman enters the autonomous agent market
Wingman enters a growing category of software agents that run in the background to complete tasks, a space that includes tools such as OpenClaw and Claude from Anthropic. Emergent’s approach is to embed Wingman into the messaging apps people already use—specifically WhatsApp, Telegram, and Apple’s iMessage—reducing the need for users to adopt a new interface.
From vibe-coding to execution
Emergent initially gained attention for its vibe-coding platform, which lets users without technical backgrounds build full-stack applications using natural-language prompts. The platform competes with tools such as Cursor and Replit.
Wingman shifts the company’s focus from building to operating. Co-founder and CEO Mukund Jha said, “The obvious next step for us was, can we help them not just build the software, but actually operate more autonomously through it?” He added that the move is from software that supports a business to software that “can actively help run it.” (TechCrunch)
Wingman is designed to manage tasks and workflows through chat while performing work across other tools. Users can assign and monitor tasks via messaging platforms, and Wingman can run in the background across connected systems including email, calendars, and workplace software. The agent can perform routine actions autonomously but seeks user approval for more consequential steps.
Design approach: messaging-first interface and trust boundaries
A key design decision is where users interact with the agent. Rather than presenting a new dashboard, Emergent is embedding Wingman into messaging platforms—WhatsApp, Telegram, and Apple’s iMessage—so users can interact with it via chat. Jha explained that “A lot of real work already happens through chat, voice, and email—asking for something, following up, sharing context, making a decision,” and that “Increasingly, they’ll be the main ways we work with agents too.” (TechCrunch)
The agent uses an approval workflow for more consequential actions, requiring user confirmation before executing significant tasks. This approach is designed to balance automation with user control.
Market context and competitive landscape
Wingman enters a competitive space for autonomous AI agents. OpenClaw—previously known as Clawdbot and Moltbot—has gained traction among early adopters. Other players including Anthropic and Microsoft are also developing agent-based systems.
Emergent’s approach targets a specific workflow: tasks initiated and coordinated through everyday messaging, then executed across other productivity tools. The rollout begins with a limited free trial, after which access will be paid. Existing Emergent users can access the agent through their accounts.
Company scale and funding
Emergent has more than 8 million builders using its vibe-coding platform and over 1.5 million monthly active users. The startup was founded in 2025 and raised $70 million in January at a $300 million valuation, with backing from SoftBank, Khosla Ventures, and Lightspeed Venture Partners. (TechCrunch)
These figures suggest Emergent is leveraging its existing user base to drive adoption of a new agent workflow. Users who previously used vibe-coding for software creation may transition to execution-oriented automation through Wingman.
Current limitations
Wingman has limitations common to emerging agent systems. Jha noted that the system struggles with “consistency in really ambiguous situations, messy edge cases, unclear goals, or workflows where a lot of human judgment is needed.” (TechCrunch)
Agent performance depends heavily on how clearly tasks can be specified and how robustly the system interprets intent under uncertainty. If Wingman requires user approval for consequential steps, ambiguous requests could become a bottleneck—either forcing more approvals or leading the agent to underperform when it cannot confidently disambiguate goals.
In the near term, teams may prefer structured requests and well-defined routines with Wingman, while reserving complex decision-making for human oversight. As agent capabilities mature, improvements in consistency and edge-case handling could expand the system’s utility.
Source: TechCrunch