On 24 September, Agoda published the AI Developer Report 2026, subtitled “Agentic AI Adoption Outpaces Enterprise Readiness Across Southeast Asia and India.” It surveys more than 800 developers and engineering leaders across seven markets — Indonesia, Malaysia, Thailand, the Philippines, Singapore, Vietnam and India — with fieldwork run online between June and August. Most of the coverage this week has led on the adoption numbers. The number we cannot stop looking at is the gap between two of them.
53 Percent Are Doing It. 38 Percent Think They Should Be.
Fifty-three percent of respondents say AI agents are already used in selected production workflows or broadly across their organisation. Thirty-eight percent describe their codebase as mostly or fully ready for a fully autonomous agent. Those two figures come from the same survey, answered by the same people, and the distance between them is the entire story.
It is not a contradiction and it is not recklessness. It is what adoption looks like when the tooling arrives faster than the codebase can be prepared for it, which is the normal condition of every technology shift. But it has a specific and immediate consequence for anyone hiring: if a meaningful share of teams are running agents against code they do not consider ready, then the work of catching what the agent got wrong is not a temporary phase. It is the job.
The report says as much in its own numbers. 86 percent of respondents still review or validate AI-generated output always or most of the time. 79 percent require human approval before code reaches production. Agoda CTO Idan Zalzberg framed the shift in the release as follows: “What began as a way to speed up individual tasks is becoming a broader change in how software is built.”
Our Expert Take #1
Read the 86 percent as a workload statement, not a trust statement. Every one of those reviews is an hour of senior engineering time that used to be spent producing and is now spent verifying. The report also finds 55 percent saving at least seven hours a week, up from 18 percent a year earlier — so the hours are real, they have simply moved. For a Singapore employer this reframes the headcount question entirely. The bottleneck in most teams we scope is no longer how fast code gets written; it is how fast it can be responsibly approved. Hiring another fast producer into that does not help.
The 4 Roles We Rewrote This Week
We went back through the Singapore briefs we are currently running and changed four of them. Not the seniority, not the salary band — the description of what the person is for.
- “Senior backend engineer” became “senior engineer, review and integration.” The deliverable is no longer feature throughput. It is the quality of what passes the gate, and the design of the gate itself.
- “Full-stack developer, 3 to 5 years” grew an explicit evaluation component. The interview now centres on reading a plausible implementation and finding what is wrong with it, rather than producing one from scratch.
- “Junior developer” became a named apprenticeship with a reviewer attached. More on why below, because this is the one we feel strongest about.
- “Platform engineer” picked up a line on cost observability for AI workloads. A phrase that did not appear in any brief we wrote a year ago, and now appears in several.
That last one comes straight out of the report’s barrier ranking, which surprised us. Cost is the leading obstacle at 28 percent, ahead of integration complexity at 24 percent and lack of governance at 19 percent. Quality does not top the list. Teams have largely contained quality risk through review gates — that is what the 79 percent approval requirement is — while spend scales with usage in a way that is invisible until the invoice lands.
The Junior Problem Singapore Is About to Create for Itself
The finding in this report that should worry Singapore employers most is not about tooling at all. 49 percent of junior developers report feeling less secure about their future prospects — against 17 percent of CTO and VP Engineering respondents expressing career concerns. That is a wide gap in how the same change is experienced at two ends of the same organisation.
Sentiment alone would not worry me. What worries me is that the sentiment is tracking something structurally true. The traditional apprenticeship for a junior engineer ran through volume: write the straightforward endpoint, write the form validation, write the test, and through repetition acquire judgement. That is precisely the body of work agents now absorb. Remove it and the path to judgement is gone unless someone deliberately replaces it.
The tempting response — stop hiring juniors, hire only seniors who can review — is a trap with a three-year fuse. Seniors are produced by organisations that grew them, and if every employer in Singapore stops growing them simultaneously, the market for the reviewers everyone now needs gets tighter every year while nobody replenishes it. We have said this before in a different context and the mechanism has not changed; what has changed is that the report gives it a number.
Our Expert Take #2
Our position, which not every client agrees with: keep hiring juniors in Singapore, but stop hiring them as producers. Write the role as a review-and-verification apprenticeship, pair each one to a named senior whose job description includes the mentoring, and measure the junior on defects caught rather than tickets closed. Done deliberately, this produces a competent reviewer faster than the old volume-based path did, because the feedback loop is tighter — you find out within a day whether they can spot a bad implementation. Done carelessly, you produce someone whose entire skill is accepting suggestions, and that person is genuinely hard to promote.
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Get 3 Free Developer ProposalsWhat This Does to the Interview
If 86 percent of the work involves validating output, then an interview built entirely around producing output is measuring the wrong half of the job. We have moved our Singapore technical screens accordingly, and the change is smaller than it sounds.
Instead of asking a candidate to write an implementation under time pressure, we hand them a working one — plausible, well-formatted, of the kind an agent produces — containing a real defect. A subtle concurrency issue. A missing authorisation check. An error path that silently swallows failures. Then we ask them to review it the way they would review a colleague’s pull request.
What this reveals is whether someone reads code adversarially or trusts output that looks right, and that single distinction now predicts on-the-job performance better than anything else we test. We pair it with one question that is very hard to fabricate: tell me about a time you rejected AI-generated code, and why. People who have genuinely worked this way answer it in specifics within seconds. People who have not, generalise.
Colleagues running the same problem in the UAE have written up the mechanics in more depth — their guides to structuring the AI engineer technical interview and to evaluating take-home assessments when candidates have the same tools you do both translate directly to the Singapore market.
Our Expert Take #3
One caveat we would apply to every number above, including the ones we have just built four job briefs on: this is a seven-market regional sample of 800-plus people, not a Singapore census, and it is published by a company with its own engineering brand to promote. That does not make it wrong — the internal consistency is good and the direction matches what we see in our own Singapore pipeline — but treat 53 and 38 as shape, not as precision. The finding we would actually bet on is the ordering: adoption ahead of readiness, review absorbing the difference, and cost rather than quality as the binding constraint. That ordering is what should drive your next hire, not the second decimal place.
FAQ — What Singapore Employers Are Asking Us
What exactly did the Agoda AI Developer Report 2026 measure?
Agoda published the report on 24 September 2026, based on an online survey of more than 800 developers and engineering leaders across seven markets: Indonesia, Malaysia, Thailand, the Philippines, Singapore, Vietnam and India. Fieldwork ran between June and August 2026. The headline findings are that 53 percent of respondents already use AI agents in selected production workflows or broadly across the organisation, while only 38 percent describe their codebase as mostly or fully ready for a fully autonomous agent, and that 86 percent still review or validate AI-generated output always or most of the time. A further 79 percent require human approval before code reaches production. It is a regional sample rather than a Singapore-only one, which is a limitation worth holding in mind, but Singapore is one of the seven markets and the pattern is consistent enough across them to be useful for planning.
Does this mean I should stop hiring junior developers?
No, and we would argue the opposite, though the job has to change. The report found that 49 percent of junior developers feel less secure about their future prospects, which tells you about sentiment rather than about whether the role has value. The practical issue is that the traditional junior apprenticeship ran through writing large volumes of straightforward code, and that is exactly the work agents now absorb. If you hire a junior into a team where an agent writes the first draft and nobody teaches them to evaluate it, you produce someone who can accept suggestions and not much else. If you hire a junior explicitly into a review-and-verification apprenticeship, with a senior engineer who explains why a given output is wrong, you produce a reviewer faster than the old path produced one. Singapore employers who stop hiring juniors entirely will find in three years that they have no mid-level engineers and no internal path to seniority, at which point they will be bidding for the same scarce seniors as everyone else.
If agents write the code, why is cost the top barrier rather than quality?
Because quality has become a managed problem while cost has become an unmanaged one. In the report, cost is the leading barrier at 28 percent, ahead of integration complexity at 24 percent and lack of governance at 19 percent. Teams have learned to contain quality risk through review gates, and 79 percent requiring human approval before production is precisely that containment working. Spend, by contrast, scales with usage in a way that headcount does not: an agent that runs in a loop consumes tokens in proportion to how hard the task is and how many times it retries, and that consumption is often invisible until the invoice arrives. This is why we have started seeing Singapore job descriptions that mention cost observability for AI workloads, a phrase that barely existed a year ago. It is also a reason to be sceptical of any business case that models agents as a straight substitution for salary.
How should this change what I test for in an interview?
Move the centre of the interview from production to evaluation. The classic exercise asks a candidate to write a function under time pressure, which measures something agents are now demonstrably competent at. A more informative exercise hands the candidate a working, plausible, agent-generated implementation that contains a real defect — a subtle concurrency issue, a missing authorisation check, an error path that swallows failures — and asks them to review it as they would a colleague pull request. What you learn is whether they read code adversarially or trust output that looks right, and that distinction is now the single most valuable thing you can know about a hire. We also ask candidates to describe a time they rejected AI-generated code and explain the reasoning, because the answer is very hard to fabricate and immediately separates people who have genuinely worked this way from people who have read about it.
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Source: Agoda AI Developer Report 2026: Agentic AI Adoption Outpaces Enterprise Readiness Across Southeast Asia and India, released 24 September 2026 via PR Newswire APAC; regional coverage by TechNode Global, 25 September 2026. Survey of 800+ developers and engineering leaders across Indonesia, Malaysia, Thailand, the Philippines, Singapore, Vietnam and India, fielded online June to August 2026. Quotation from Agoda CTO Idan Zalzberg as published in the release.