Why Agent Hero?

A business is its people: what they know, how they do things, who they know. That know-how is what lets a company grow, and most of it is never written down.

It doesn't stay in one place. The median US worker has been with their employer for 3.9 years,¹ and half of employees worldwide are watching for or seeking a new job.² 72% of employers struggle to fill roles, and AI skills are now the hardest to find.³

Gaps are expensive. In small firms, losing a worker unexpectedly costs one to two years of their pay.⁴ Projects stall, colleagues cover, and the next person relearns what the last one knew. Leave works the same way on a smaller scale: the work only one person knows how to do waits for them.

AI can draft, analyse, research and carry out work, and 75% of knowledge workers already use it.⁵ But almost every AI product is designed around the individual.

People bring their own. 78% of AI users bring their own AI tools to work, rising to 80% at small and medium-sized companies.⁵ 70% of employees who use AI rely on free public tools, and 57% hide how they use it.⁶

Even business plans are personal underneath. Chats, custom assistants, instructions and skills sit in each user's account, and what a person never shared stays with them.⁷

The AI learns the person, not the project, the department or the company. It reaches company data through connectors, but what it learns about how the work is done belongs to whoever was using it.

So the gains don't add up. 88% of companies use AI regularly, but only 39% see an effect on profit across the company, and nearly two-thirds haven't started scaling it.⁸ Researchers trace failed AI pilots to a “learning gap”: tools that don't retain feedback or adapt to how the organisation works.⁹

We believe every business needs its own AI agents: agents that belong to the company, understand how it works, and stay with it as the team changes.

We believe the whole team should work through them. People set the direction, share their experience and make the decisions. The agents carry the work, 24/7.

What that looks like. Today, a bookkeeper has one AI account and a finance analyst has another. Each AI learns its own user, and neither learns the finance team. With Agent Hero, the whole finance team works through one finance agent, with the team's documents, tools, context and skills. They discuss the month-end close inside it, so it knows what they decided and why. When one of them has it reconcile the month, the method is checked and saved, and the next person gets a faster, better result.

Agent Hero comes from a product-design view of the problem. Most AI products start from one person, one assistant, one history. A few let teams share an agent, usually as a bot in a chat app or a separate workspace, so the agent, the conversation, the documents, the permissions and the history all live in different places. We started from the other end.

• Teams come into the agent. Each agent holds a role, such as Finance, Sales or Operations. The people who do that work join it, talk to each other inside it, and work through it.

• Every agent owns a computer. Its own documents, databases, browser, tools and skills, kept between tasks, so work is carried through to an outcome.

• The team's conversation is the agent's context. Chats happen inside the agent, not in a separate app, so the reasons behind the work stay with the work.

• Connectors are agents too. Each connection to outside software runs as its own agent, with its own permissions. It's a means to an end, not where the work lives.

• The agent can be the software. Agents build apps and screens, maintain databases, hold documents and manage who can access what, so a business needs fewer separate tools.

• One place to govern everything. People, data, documents, apps, skills and permissions all sit in the agent, not across a patchwork of bots and accounts.

• People guide; skills compound. A team member guides every task and signs off the result. The methods that work are saved as skills the whole team reuses.

• Always the best models, never locked in. Every new frontier model from OpenAI, Anthropic and Google is available as soon as it's released, and the team chooses which to use. We track the releases so businesses don't have to.

Agents can do real work. Work that wasn't practical to hand to an agent two years ago now is, and it gets cheaper every year.

Agents can connect to business tools. The Model Context Protocol is the shared standard for connecting AI to business tools and data. ChatGPT, Gemini and Copilot support it, and since December 2025 a neutral foundation governs it.¹⁰

The market is moving from assistants to agents. 82% of business leaders expect to use AI agents as digital labour within 12 to 18 months.¹¹

The habit exists, in the wrong shape. 75% of knowledge workers already use AI at work, mostly in personal silos.^5,6 The open question is who owns what that AI learns: the person, the AI vendor, or the business. We think it should be the business.

The problem exists in every business, but SMEs feel it most and are served least.

They feel it most. 63% of Singapore SMEs name manpower cost their No. 1 challenge,¹⁵ 83% of employers say those costs are rising,¹⁶ and 63% of small employers planned a 2026 hiring freeze.¹⁷ The average SME worker produces S$132k of value a year against S$349k at larger firms,¹⁸ so SMEs can't match big-company pay. In a ten-person team, one absence takes out a tenth of the business.

They're served least. 14.5% of Singapore SMEs use AI, against 62.5% of larger firms.¹⁹ 46% of large firms using AI had it built or adapted by a specialist, against 26% of small firms.²⁰ Singapore's deep-AI support requires an in-house tech team.²¹ Personal AI is all most SMEs have.

They can be reached. Singapore has 369,500 SMEs: 99% of enterprises and 69% of employment.²² Professional-services firms are adopting AI fastest, from 5.1% to 25.7% in one year.¹⁹

The product is the same for a 10-person firm and a 1,000-person company. SMEs are where the need is sharpest and the competition weakest, so that's where we prove it first.

People decide; agents carry the work. The team brings judgment, experience and relationships. The agents handle the work, 24/7.

Know-how stays with the business. Methods and context are already in the agent colleagues use.

Leave without the scramble. Work keeps moving while someone is away, without colleagues covering it from scratch.

Newcomers start at full speed sooner. They work through an agent that already knows how the team does things.

Capacity grows faster than headcount. New initiatives no longer wait for a hire.

These outcomes have limits. Agents carry digital work; client relationships, judgment calls and sign-off on anything that matters stay with people. The best AI agents complete only about 30% of realistic office tasks on their own,²³ and only 22% of business owners fully trust AI to work unsupervised,²⁴ so a person guides every task.

For every customer we measure work completed against the time and cost to deliver it, whether saved skills reduce the guidance later tasks need, whether a colleague can pick up work without rebuilding its context, and how quickly new joiners become productive. The first results are in.

Know-how held in a shared system makes new people productive sooner and makes each person more capable.

New staff perform like experienced staff, sooner. When AI captured how top performers worked, in a study of 5,179 employees, staff with two months' experience performed like those with six months.²⁵

A person with AI does the work of a small team. In a field experiment at Procter & Gamble, one person working with AI matched the performance of a two-person team.²⁶

AI fills skills gaps. 39% of SMEs using generative AI with a skills gap say it helped make up for missing skills, rising to 49% where AI is used in core work.²⁷