AI agents are set to transform digital work by executing complex tasks across tools and platforms. For Bangladesh, the opportunity isn’t in building infrastructure, but in building smart solutions powered by local talent and ingenuity
Illustration: TBS
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Illustration: TBS
When the tractor was first invented in 1892 in a small village in Northeast Iowa, USA, farmers weren’t sure whether to farm with it or run from it. It was noisy, greasy, and looked like a steam engine with ambition.
But those who dared to try it quickly discovered its true power: it could automate repetitive, back-breaking workflows such as ploughing and tilling. Instead of spending days on these tasks, farmers could now complete them in hours. Those who adopted tractors early were able to scale their operations, grow more, and harvest faster—transforming agriculture from subsistence to surplus.
Fast forward to 2022—OpenAI took the world by storm with ChatGPT, sparking an AI boom. What began as a clever chatbot rapidly evolved into a global phenomenon: writing code, crafting essays, debugging spreadsheets, and even offering relationship advice (whether solicited or not). It felt like magic in a text box, and suddenly, everyone—from students to CEOs—wanted a piece of the AI pie.
While chatbots are a compelling application, what I believe will be the next tractor—beyond the hype—is the AI agent. An AI agent is not merely a tool that chats; it is a system that can perceive, decide, and act. It takes a goal, figures out the necessary steps, and uses real-world tools—such as search engines, web browsers, or even your own computer—to execute tasks on your behalf.
OpenAI’s Operator, for instance, can search the web, read PDFs, run code interpreters, and take actions across applications. Anthropic’s Claude Computer Use operates a virtual machine—opening files, browsing folders, and editing documents autonomously. Amazon’s Nova Act SDK enables developers to build agents capable of completing web-based tasks such as submitting out-of-office requests, placing calendar holds, or setting auto-replies.
These agents don’t just assist—they deliver. Like the tractor that revolutionised fieldwork, AI agents are poised to transform digital workflows, making them the true disruptors of this technological wave.
To capitalise on this once-in-a-generation opportunity, major tech companies are investing heavily. Microsoft has committed $80 billion to AI infrastructure for the fiscal year 2025. Meta has increased its 2025 capital expenditure forecast to strengthen AI and infrastructure. Google expects to invest approximately $75 billion in capital expenditures in 2025. Amazon plans to spend over $100 billion this year, primarily on infrastructure to support AI—surpassing even Microsoft and Google.
For a developing country like Bangladesh, these figures are staggering. Competing on capital is unrealistic—but the opportunity lies elsewhere.
Bangladesh can play to its strengths: a young, tech-savvy population, rapidly growing internet access, and a thriving freelance and startup ecosystem. By focusing on AI-based application development instead of infrastructure, Bangladesh can leapfrog into the global AI conversation.
Building lightweight, locally relevant AI agents that solve real-world problems demands creativity more than capital. In this global AI race, Bangladesh doesn’t need to build the racetrack—it needs to train smart runners.
Startups in Bangladesh must be strategic and focused to succeed in the AI space. They can tap into the country’s massive freelance workforce, many of whom are already skilled in SEO, data entry, and transcription. With the right coordination and tooling, this talent pool could power data annotation ventures similar to Scale AI—providing essential training data for global tech firms building LLMs, vision models, and AI agents.
Startups should also address local challenges with scalable solutions. The success of Pathao illustrates that Bangladesh’s market is hungry for context-aware platforms. There is ample scope for AI agent-powered innovations in industry logistics, healthcare triage, and agricultural optimisation.
The education sector, too, is ripe for transformation. 10 Minute School stands as a strong example of innovation in this domain. As public concern grows over declining textbook quality and outdated curricula, there is a clear opportunity to develop LLM-based Bangla tutoring agents. These would not just answer questions but explain content drawn from high-quality academic sources—reintroducing intellectual rigour through modern delivery.
Developers can actively participate in this revolution by contributing to open-source LLM projects such as Ollama, which simplifies on-device model inference. There is room for coders, tinkerers, and machine learning enthusiasts to contribute—whether by adding support for new models, improving inference pipelines, or building agent toolkits.
One impactful avenue is closing the language gap by training or fine-tuning LLMs specifically for Bangla. This work can begin with open-source models like Mistral or LLaMA, using Bangla datasets such as news articles, textbooks, or Wikipedia dumps. The results can be shared on Hugging Face—a popular open-source platform for hosting and collaborating on machine learning models. Hugging Face operates like GitHub for AI, allowing users to upload models, datasets, tokenisers, and related tools. Contributing Bangla-specific models enhances the language’s visibility and usability in the global AI ecosystem.
To enable this kind of innovation, the government must act as a catalyst—not by matching big tech’s spending, but by empowering its human capital. One high-impact move would be to subsidise or provide credits for GPU cloud access on platforms like AWS, GCP, or Azure, helping startups and researchers overcome prohibitive compute costs.
Additionally, the government could organise workshops, bootcamps, and national hackathons focused on AI agent development. These initiatives would equip developers with the skills and toolkits required to build practical solutions tailored to local needs.
By seeding opportunities and reducing friction, Bangladesh can position itself as a testbed for pragmatic, problem-solving AI—fuelled not by vast infrastructure, but by local ingenuity.
Sketch: TBS
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Sketch: TBS