What we do

Agentic AI, engineered to ship

We help teams across Southeast Asia turn AI agents from impressive demos into reliable, production systems — by auditing the harness, embedding delivery teams, and building the skills that stay.

See how we help

Most AI projects don't fail at the model. They fail at everything around it — the context the agent sees, the tools it can call, the loop that decides when it's done, and the verification that catches it when it's wrong. That scaffolding is where the value and the risk live, and it's where we work. Inference Loops is an agentic-AI consultancy built from a Cambodia base, helping organizations design, deploy, and own the systems that make agents trustworthy.

Three ways we work with you

Whether you need a hard look at a system that isn't behaving, a team that can build one, or people who can run it after we leave — we meet you where you are.

01

Agentic AI Audits & Harness Engineering

A rigorous review of the scaffolding around your model — and the fixes that make it reliable.

The problem

Your agent works in the demo and breaks in production. It loses the thread on long tasks, calls the wrong tool, burns tokens on bloated context, or ships confident, wrong output that nobody catches in time.

What we do

We audit the whole harness — context management, tool dispatch, the agent loop, guardrails, and verification — then re-engineer the weak points. You get a clear report of what's fragile and a hardened system that behaves the same in production as it did in the demo.

How it works

  1. 1

    Map the loop

    We trace how your agent gathers context, takes action, and verifies results — and where it actually breaks.

  2. 2

    Stress the harness

    We probe context rot, tool failures, runaway loops, and the gaps in your verification gate with realistic, adversarial inputs.

  3. 3

    Re-engineer the weak points

    Compaction and context strategy, scoped tools, circuit breakers, iteration caps, and an independent verifier — built into the harness, not the prompt.

  4. 4

    Hand over the evidence

    A prioritized findings report, the hardened harness, and the evals that prove it stays fixed.

An agent you can trust to run unattended — fewer failures, lower token cost, and a verification gate you'd stake a release on.

02

Embedded Delivery Teams

Experienced agentic-AI engineers, embedded in your org, shipping from day one.

The problem

You know agents could transform how you build — but you don't have the in-house experience to do it safely, and hiring a full team for an unproven bet is too slow and too risky.

What we do

We embed a small, senior team that already knows how to design loops, orchestrate sub-agents, write the specs and evals, and ship production agentic software. They work inside your codebase and your workflow, and they leave your people more capable than they found them.

How it works

  1. 1

    Scope the work

    We define the first high-leverage project together — something real, shippable, and worth doing.

  2. 2

    Embed the team

    Senior agentic-AI engineers join your repo, your standups, and your tools — working as part of your team, not a black box.

  3. 3

    Ship in loops

    Spec, build, verify, ship — in tight iterations, with the harness and evals built in from the start.

  4. 4

    Transfer ownership

    We document the system and upskill your engineers as we go, so the work outlives the engagement.

Production agentic software shipped on a real timeline — and an internal team that can carry it forward without us.

03

Training & Capability Building

Workshops and upskilling that turn your engineers into agentic-AI builders.

The problem

Your developers are capable, but agentic AI is a different skill — loop design, context engineering, specs, and verification — and generic online courses don't teach how to ship it on your stack and your problems.

What we do

We run hands-on workshops and structured upskilling tailored to your team and codebase — from the fundamentals of how agents reason to the harness patterns that make them reliable. Practical, project-based, and grounded in the Southeast Asian context your team actually works in.

How it works

  1. 1

    Assess the team

    We gauge where your engineers are and what they need to build — no one-size-fits-all curriculum.

  2. 2

    Hands-on workshops

    Live, project-based sessions on loop design, context engineering, spec-driven development, and verification.

  3. 3

    Build on your stack

    Trainees apply each pattern to a real problem in your codebase, not a toy example.

  4. 4

    Embed the practice

    We leave behind playbooks, AGENTS.md conventions, and review habits so the capability sticks.

Engineers who can design, ship, and maintain agentic systems on their own — and a team that keeps getting better after we leave.

Why Inference Loops

We engineer the loop, not the hype

Our entire thesis is that the value in modern AI is the engineering around the model — the harness, the loop, the verification. It's what we write about, and it's what we build.

Built for Southeast Asia

We work from a Cambodia base and understand the region's languages, constraints, and opportunity. Small, sharp teams here can compete on output, not headcount — and we help them do it.

We leave you more capable

We're not here to make you dependent on us. Every engagement transfers knowledge, so your team owns the systems we build together long after we're gone.

Let's put agents to work

Tell us what you're building or what isn't working. We'll tell you honestly whether we can help — and how.

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