Across Southeast Asia’s technology hubs, the AI conversation has moved quickly from “Can it help developers write code faster?” to a harder question: “Can it be trusted to work on the codebase by itself?”
That shift matters. The first wave of generative AI in software engineering was largely about assistance: autocomplete tools, chat-based coding helpers, boilerplate generation and faster documentation. The next wave is different. Autonomous AI agents can plan multi-step tasks, inspect repositories, refactor code, generate tests, review pull requests and attempt end-to-end software changes with less direct human prompting.
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For founders and CTOs, especially in fast-moving startup markets such as Singapore, Vietnam, Indonesia, Thailand, Malaysia and the Philippines, the appeal is obvious. Engineering talent is expensive, product cycles are short, and investors expect teams to do more with less. If AI agents can help small teams ship faster, the productivity upside is difficult to ignore.
But new data suggests Southeast Asia may be entering an uncomfortable phase: companies are deploying autonomous agents faster than their engineering systems are ready for them.
According to the Agoda AI Developer Report 2026, which surveyed more than 800 software developers and technology executives across Singapore, Thailand, Vietnam, Malaysia, Indonesia, the Philippines and India, 53 per cent of developers say AI agents are already in production or broad organisational use. Adoption is higher in several key markets: 58 per cent in Singapore, 64 per cent in Thailand and 59 per cent in Vietnam.
Yet only 38 per cent of developers believe their codebase is mostly or fully ready for full autonomy.
That gap between usage and readiness could become one of the defining engineering risks for startups in the region.
Productivity gains are real, but so are the risks
The report captures why founders are moving quickly. Across the region, 55 per cent of developers say AI tools now save them at least seven hours a week, up sharply from 18 per cent a year earlier. In Thailand, 70 per cent of developers report saving seven or more hours weekly. In Vietnam, the figure stands at 63 per cent.
Developers are also no longer restricting agents to simple tasks. The report shows they are testing or using agents across much of the software lifecycle: 71 per cent for documentation generation, 70 per cent for code refactoring, 66 per cent for autonomous code generation, 63 per cent for research and 62 per cent for pull-request reviews.
For a startup, that can translate into faster feature releases, shorter debugging cycles and more time for senior engineers to focus on architecture rather than repetitive implementation work. In a region where many companies operate across fragmented markets, multiple languages, different regulatory systems and uneven infrastructure, any tool that improves engineering velocity can feel strategically important.
The problem is that autonomous agents do not understand a company’s software the way experienced engineers do. Much of a startup’s codebase is shaped by context that is rarely written down: why a workaround exists, which payment flow breaks in a specific market, which customer segment depends on an old API, or which “temporary” patch has quietly become mission-critical.
A human engineer can often infer those trade-offs from experience, Slack history or conversations with colleagues. An AI agent, by contrast, depends on what it can read, retrieve and reason through. If the codebase is poorly documented, tightly coupled, weakly tested or filled with hidden dependencies, the agent may make confident but flawed assumptions.
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That is where productivity can turn into technical debt.
Startups face the sharpest version of the problem
The readiness gap is particularly relevant for early-stage companies. The Agoda report found that among startups with one to 50 employees, 63 per cent report production or broad AI agent adoption, but only 37 per cent say their codebase is structurally prepared for autonomous execution.
The same pattern appears in larger growth-stage companies. Among organisations with 201 to 1,000 employees, 63 per cent report active production or broad agent use, while only 32 per cent believe their codebase is ready.
This is not surprising. Southeast Asian startups often build under pressure. Teams optimise for product-market fit, market launches, fundraising milestones and customer growth. Clean architecture, internal documentation, automated tests and platform governance can fall behind.
That trade-off is understandable in the early days. But autonomous AI changes the cost of messy systems. A codebase that is merely annoying for humans can become actively dangerous for agents. An undocumented dependency may lead to a broken workflow. A missing test may let a regression into production. A vague instruction may result in an agent modifying the wrong part of the system.
In other words, AI does not eliminate technical debt. It can expose it, accelerate it or multiply its consequences.
What “agent-ready” really means
The report points to five areas that companies need to assess before giving agents more autonomy.
The first is technical readiness. A codebase needs to be modular, decoupled and supported by reliable tests. If an agent cannot isolate the impact of a change, it is more likely to break adjacent services.
The second is integration readiness. Agents need access to the right development environments, internal packages, sandboxed databases, continuous integration pipelines and testing systems. Without these, they may generate code that looks plausible but cannot be safely validated.
The third is economic readiness. AI agents consume tokens, make repeated attempts and sometimes fail before producing useful output. Startups need visibility into the cost per successful task, not just the novelty of automation.
The fourth is governance readiness. Companies must define which tasks agents can complete independently, which require human approval and which are off limits. This is especially important in regulated sectors such as fintech, healthtech, insurtech and digital lending, where a faulty deployment can have legal or consumer-protection implications.
The fifth is workforce readiness. Engineers need to learn how to brief agents, review outputs, design better systems and validate changes. The role shifts from writing every line of code to directing, constraining and auditing automated execution.
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As Siba Prasad Hota, Senior Technical Lead and Architect at Sakhatech Information Systems, puts it in the report: “The goal should be AI autonomy with human oversight where consequences are significant, rather than replacing human ownership entirely.”
The Omise example
Regional payments company Omise offers a useful example of a more controlled approach. Rather than letting developers run unconstrained agents across production repositories, the company built a governed agentic platform for impact analysis, feature development, testing and code review.
During a major platform upgrade, Omise used AI agents to assess downstream code impacts, helping engineers complete in weeks what would previously have taken months. According to Sylvain Dormieu, Director of Engineering at Omise, the company achieved a 37 per cent productivity gain based on story points.
The more important lesson is not simply that AI improved output. It is that Omise treated agents as part of an engineering system, not as a shortcut around one. Junior developers could generate baseline code faster, while senior engineers focused on business logic, architectural decisions and risk. Accountability remained human-led.
That distinction is crucial for Southeast Asia, where startups are often scaling across markets with different payment rails, compliance requirements, logistics networks and consumer behaviours. Autonomy without context can be brittle. Autonomy inside a governed system can be powerful.
The founder’s takeaway
For startup leaders, the message is clear: high AI adoption does not automatically equal engineering leverage.
The companies that benefit most from autonomous agents will not necessarily be those that use the most tools. They will be the ones that make their systems legible to machines and accountable to humans.
That starts with refactoring for context: breaking down monoliths where practical, improving internal documentation, strengthening tests and making dependencies clearer. It also means building gateways rather than blanket bans, so agents operate inside controlled environments with automated checks. Finally, companies need to match autonomy to risk. Documentation, test scaffolding and research may be suitable for greater automation. Production deployments, security-sensitive changes and customer-facing systems still require strong human review.
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Southeast Asia’s startups have often won by moving faster than incumbents. In the age of AI agents, speed will still matter. But the advantage may shift towards teams that combine speed with discipline.
The next phase of software engineering will not be defined by whether companies use autonomous AI. Many already do. It will be defined by whether their codebases, workflows and leadership teams are ready for what autonomy actually demands.
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