AI Agent Development Services

Anyone can wire an LLM to a few tools and call it an agent. Making one that reliably completes real work — across many steps, with messy inputs, without going off the rails — is engineering. Krazimo’s AI agent development services build that second kind: autonomous and multi-agent systems that hold up in production, designed by ex-Google engineers who have shipped systems at scale.

What we build

From a single task-focused assistant to a coordinated team of agents, we build the agent, the tools it calls, the guardrails around it, and the monitoring that keeps it honest:

• Autonomous task agents — agents that plan, call tools and APIs, and complete multi-step jobs with human-in-the-loop checkpoints where it matters.
• Multi-agent systems — multiple specialised agents that coordinate, hand off, and check each other’s work on complex workflows.
• Tool & system integration — agents wired into your real stack (CRM, ticketing, databases, internal APIs), not a sandbox.

Multi-agent systems

Single agents stall on long, branching work. We design multi-agent systems where a planner decomposes the job, specialist agents execute, and a verifier agent reviews — a pattern that catches the failures a lone agent silently ships. We choose the orchestration framework for the job rather than forcing one architecture on every problem.

Agent evaluation & reliability

This is our edge. Agents fail in ways traditional software doesn’t — they hallucinate steps, loop, or quietly take the wrong action. Before anything ships, we build an evaluation harness that scores the agent on real tasks, and once it’s live we monitor for regressions and drift. You get an agent you can trust to run unattended, not one you have to watch.

Why teams bring us in

We cap active work at ten projects, so senior engineers do the build, not juniors learning on your budget. Agent work often pairs with broader intelligent workflow automation or a wider custom AI software development engagement. If you have a multi-step process you wish ran itself, book a demo and we’ll scope whether agents are the right tool.

How an AI agent development engagement works

Agents fail in ways normal software doesn’t, so we de-risk in phases: discovery (which steps are genuinely worth automating, where a human must stay in the loop), a scoped pilot on one real workflow with a defined success metric and a risk-free trial, then build & evaluate against an agent eval harness, and finally deploy & monitor for regressions and drift. The same senior engineers run all four — we cap active work at ten projects.

A real agent we shipped: Chip Inc

For Chip Inc, we built an AI-powered research assistant that actually runs the work — automating the tedious parts of research (gathering sources, cleaning data, rerunning experiments) while still supporting serious, reproducible computation and carrying project memory across a workflow. It’s a working example of an agent that does multi-step work reliably, not a chatbot that just answers.

When you need agents vs. simple automation

Reach for agents when the work is genuinely multi-step, branching, and needs judgment at each step — research, operations, or customer workflows that a fixed script can’t handle. If the process is repeatable and rule-shaped, plain workflow automation is cheaper and more reliable, and we’ll tell you so. The honest answer is often a mix: agents for the judgment, deterministic automation for the rest.

Another agent in production: phone support that runs itself

We also built agents that automate the “basic questions” layer of phone support across industries. For businesses with simple, repeatable workflows — restaurants, for example — human involvement dropped to as low as 2–3%, with faster responses and fewer missed calls, including after hours. An agent doing real operational work unattended, which is the whole bar.