Blog
Insights, analysis, and perspectives on AI in Cambodia and the region.
Agentic System Design: A Complete Guide
Designing an agentic system is different from designing a normal one: you're not specifying functionality, you're specifying behavior. The hard questions are about autonomy, feedback loops, failure modes, and guardrails — not endpoints and schemas. Here's a practical guide to designing agents that stay safe as they scale: start small, log every decision, make observability a first-class feature, and wire the circuit breakers before you need them.
Cambodia's AI Opportunity: Why Now Is the Moment
A June 2026 data refresh on Cambodia's AI moment. A median age of 26, mobile connections at 121% of the population, a $150M AI market spanning 1,200 AI-powered enterprises and 5,400 AI workers, and a draft National AI Strategy aiming for $3.35–6.7B in GDP by 2030. Here's the real state of play — the momentum, the honest gaps, and why the agentic shift tilts the field toward exactly the kind of engineering Cambodia can do.
Context Engineering: The Context Window Is Your Agent's Real Bottleneck
As agents run longer, the binding constraint stops being model intelligence and becomes the context window. A 2026 benchmark found an agent given only its last five tool calls plus a running summary hit 91.6% task completion while a full-history agent managed just 71% — using 64% fewer tokens and finishing in 40% of the time. More context made it worse. Here's why 'context rot' happens, why less context produces better agents, and the practical toolkit for engineering what the model sees — a high-leverage, GPU-free discipline.
Dual-Agent Development: Running Claude Code and Codex on the Same Project
The 2026 question isn't 'which coding agent should I pick?' — it's 'how do I run both?' The strongest teams have stopped choosing between Claude Code and Codex and started routing work to each for what it does best: Claude Code for architecture and multi-file reasoning, Codex for fast parallel implementation. Here's the hybrid workflow, the tooling that makes two agents manageable, the cost math, and why this asymmetric play favors small Southeast Asian teams more than anyone.
The Bill Is the New Bottleneck: The Economics of Agentic AI
Generation got cheap. Running the loop did not. A five-step agent costs roughly 3.2x a single model call for the same work — and by 200 steps the bill is over 100x, with 62% of it spent re-reading context the agent already paid for. Here's where the money actually goes in an agentic system, the four cost levers that work, the open-weight escape hatch at a tenth of frontier prices, and why cost discipline is the sharpest competitive lever a small Southeast Asian team has.
The Reliability Gap: Why Your Best Agent Melts Down First
The leaderboard measures the wrong thing. Single-shot benchmarks like SWE-bench score an agent's capability — whether it can succeed once — but production needs reliability, consistent success across long, repeated attempts. A 2026 study of 23,392 episodes found the two diverge so hard that rankings invert at long horizons, and that frontier models have the highest meltdown rates — up to 19% — because they reach for ambitious plans that spiral. Here's the difference between capability and reliability, why the most capable model is often the least dependable, and how to measure and engineer for the metric that actually ships.
When Agents Get Worse the Longer They Run
Coding agents don't just plateau on long tasks — they actively degrade the code they're working on, turn after turn. A 2026 benchmark built to measure this found the best agent passed only 14.8% of checkpoints, with code structure eroding in 77% of trajectories and agent output running 2.3x more verbose than human repositories. Here's what the long-horizon benchmarks actually measured, why quality decays as iterations pile up, and the harness discipline that keeps a long run from rotting.
A Bad Loop Ships Bad Code Faster: Evals Are the Real Discipline
Everyone is racing to make agents act autonomously. Almost nobody is as eager to make them act correctly. But once generation is cheap and parallel, the only constraint left is trust — and a loop you can't trust just produces wrong work at scale. This is the case for evals as the new test suite: the verification gate, the agent-as-judge, the golden dataset of real failures, and why building one is the most defensible engineering a Southeast Asian team can do.
The Agentic AI Foundation: Why Open Standards Matter for Southeast Asian Developers
OpenAI and Anthropic — fierce rivals — just co-founded the Agentic AI Foundation under the Linux Foundation. With AGENTS.md in 60,000+ repos and MCP nearing 9,400 servers, agentic AI is getting its open-standards moment. Here's why that's especially good news for developers in Southeast Asia.
AI Regulation in Southeast Asia: A Comparative Overview
How ASEAN nations are approaching AI governance and what Cambodia can learn from regional leaders.
Inside the Agent Loop: The Pattern Behind Reliable AI Agents
Swapping in a smarter model rarely fixes a flaky agent. The fix lives in the loop — the phases, the context discipline, and the verification step that turn a model's intelligence into something that reliably ships. Here's the pattern, dissected.
Designing Agent Loops That Run While You Sleep
In June 2026, Peter Steinberger told developers to stop prompting agents and start designing the loops that prompt them. Addy Osmani gave that instinct a name — loop engineering. Here's the anatomy of an autonomous loop, the guardrails that keep it from burning your budget overnight, and why this is the highest-leverage skill a Southeast Asian developer can pick up right now.
AI Coding Tools in 2026: Claude Code vs Cursor vs Copilot
Three tools dominate agentic coding in 2026 — Claude Code, Cursor, and GitHub Copilot. A practitioner's comparison of how they actually differ, who each one fits, and why the tool you pick matters less than what you build on top of it.
AI Safety & Governance in Southeast Asia: What Developers Need to Know
Governance isn't just legal's problem — in Southeast Asia's fragmented regulatory landscape, it lands in your code. A practical guide for the engineers building and shipping AI across the region.
The Evolving Role of Developers in an AI-Powered World: Perspectives from Southeast Asia
The developer's job didn't disappear — it moved up the stack. From writer to orchestrator, the skills that appreciate, the brutal junior-developer squeeze, and why the same shift that threatens one model of work opens a real door for Southeast Asia.
How Coding Agents Actually Reason
If the harness is 98.4% of a coding agent, what is the other 1.6% — the part that 'thinks' — actually doing? This is a field guide to how agents reason: the Thought-Action-Observation loop, why observation beats raw cleverness, and a Meta result showing that forcing an agent to argue like a logician — not a chatbot — lifts its code judgment by double digits. For builders who want to shape the reasoning, not just rent it.
The Inference Loop: Why Coding Is Becoming a Loop, Not a Keystroke
The most important software primitive of the decade fits in ten lines: call the model, run a tool, feed back the result, repeat. Here's why that loop is eating software development — and what it means for your team.
Loop Engineering: What It Means for Southeast Asian Developers
In June 2026 the agentic-coding world got a new name for its most important skill: loop engineering. Here's what it is, the three-layer model behind it (Harness → Loop → Orchestration), and why it's the single best bet Southeast Asia's developers can make right now.
MCP in 2026: The Universal Standard — and Its Security Problem
The Model Context Protocol won the integration war. By mid-2026 it spans ~9,650 registered servers, 97M+ monthly SDK downloads, and production use at 41% of software organizations. But the same ubiquity created a vast attack surface: a study of 67,057 servers found hundreds vulnerable, leaked credentials, and hijackable maintainers. The standard plug is settled. The security of what you plug in is the new frontier — and that's a guardrail-layer opportunity for the region's builders.
Models vs Agents: The Shifting Boundary
Every few months a new model release deletes someone's clever agent scaffolding. So is the scaffolding a dead end — or the part that actually compounds? A clear-eyed look at the boundary between model and agent, and how to build on the right side of it.
Spec-Driven Development: When You Edit the Spec, Not the Code
As agents write more of the code, the human's durable artifact shifts up a level — from the code to the specification. Martin Fowler maps three maturity stages, ending in spec-as-source, where humans only ever edit the spec and code is a compiled output. Here's why the shift is happening, the honest critique that it might divest from design, and why turning local domain rules into living specs is exactly the leverage Southeast Asia's developers should grab.
The Code Agent Orchestra: What Makes Multi-Agent Coding Actually Work
2026's real shift isn't smarter agents — it's more of them, working in parallel. Anthropic's data shows multi-agent workflows delivering 2–4x acceleration, with one enterprise compressing a 24-day project into 5. But orchestrating a squad of agents is a different skill from directing one, and most teams fail at it the same way. Here's what makes the orchestra work, where it collapses, and why it's a force multiplier built for small teams.
The Harness Is the Product: Why 98.4% of an AI Agent Isn't the Model
A study of Claude Code's source found that only about 1.6% of a production coding agent is AI decision logic — the other 98.4% is harness: the scaffolding that feeds the model, runs its tools, checks its work, and decides when to stop. Here's what that number means, and why it's the most important thing for Southeast Asia's builders to understand right now.
The Harness That Rewrites Itself
We've argued all year that the harness — the scaffolding around the model — is where the real work lives. In mid-2026 a new class of research closed the loop on that idea: agents that improve their own harness, with no human engineer and no smarter model to copy from. A June arXiv paper lifted three different models by 33–60% on a hard benchmark just by letting each one rewrite its own scaffolding. Here's what self-harness is, why it doesn't make engineers obsolete, and why it tilts the field toward exactly the work Southeast Asia can own.
The $100 Billion Guardrail: Why AI's Safety Layer Is Becoming the Product
The market for AI guardrails is projected to grow from $0.7B in 2024 to $109.9B by 2034 — one of the fastest-growing categories in software. As agents stop suggesting and start acting, the defensible spend moves off the model and onto the layer between the agent and your business. Here's what a guardrail actually is as a buildable product, why ASEAN's soft-law governance turns that layer into your compliance boundary, and why that's an opening for the region's engineers.