AI Agent Development

Build AI agents that act, not just answer

Most operational drag lives in the gaps: data re-keyed between systems, repetitive judgment calls, end-of-day reconciliation, and questions that get asked a hundred times. FastTech designs and ships AI agents across the full spectrum — from deterministic pipelines that move and reconcile data, to system-to-system integrations that close gaps between platforms, to agentic AI that handles open-ended, conversational work and acts on it.

The unit of value is the same throughout: hours given back, errors removed, and decisions that happen without a human in the loop unless one is genuinely needed. We build the workflow, the integration layer beneath it, and — where it earns its place — the agent that owns the decision itself.

Agentic automation, scoped before it is scaled

We start narrow and prove it works before we scale it. A scoped process is mapped end to end, the automatable steps are separated from the ones that need human judgment, and we build the smallest agent that delivers a real result in production. Rule-based steps stay deterministic and auditable; LLMs and agents are introduced only where ambiguity, language, or reasoning actually require them — never for their own sake.

This discipline matters with agents in particular. An agent given an unbounded mandate is a liability; an agent pointed at a well-scoped problem, with the right context and clear guardrails, is leverage. We design for the second case and instrument it so you can see what it does and trust the result.

Where agents earn their place

Some of the highest-return work sits at the intersection of agents and the knowledge they operate on. For a multi-team software company scaling AI agents across its operation, our team engineered scattered context into a centralised, curated knowledge base — one source of truth every employee and every agent draws from — turning agent chaos into a company-wide capability. Good agents are only as good as the context engineered behind them.

Where the work is genuinely net-new or open-ended, we run it through our WASP method: from challenge to a working, user-tested prototype in five business days. The output is a working system, not a deck.

Evidence, not assertion

Our track record here is operational, not theoretical.

Our AI onboarding agent replaced repetitive HR onboarding work with a Claude-powered conversational agent: new hires complete onboarding through chat while an HR dashboard tracks pipeline, progress, and at-risk hires in real time — built on the Claude API, Python, FastAPI and React, dropping manual onboarding work and reducing early-weeks absenteeism.

Earlier, a Python-based automation pipeline eliminated 5+ hours of manual work per day for a trading desk, taking post-market reconciliation and admin time to zero. The same discipline applies whether the right answer is a deterministic macro, an integration, or a full conversational agent.

If you want to build AI agents that hold up in production — not a demo that impresses once — that is the work we do.