AI-Native Service
Agentic AI Engineering
We design, build, and ship AI agents and LLM systems that hold up outside the demo — with the evals, guardrails, and observability production requires.
Most AI agents work in a demo and fall apart in production. The failure is rarely the model — it's everything around it: no evaluation harness, no guardrails, no observability, no plan for the cases the happy path ignored. That gap is where we work.
Azzal Solutions builds agentic systems as real software. We design the agent's tools and control flow, ground it in your data with retrieval that actually returns the right thing, and put evaluation and monitoring in place before anything reaches users. When an agent needs to take action — not just answer — we build the tool integrations and the safeguards that make that safe.
We work across the current agent stack and stay deliberately model-agnostic, because the right model changes every few months and your architecture shouldn't. What doesn't change is the standard: an agent you can measure, monitor, and trust with a real task.
What We Do
The concrete work inside a Agentic AI Engineering engagement.
- Agent architecture: tool use, multi-step control flow, and memory
- Retrieval-augmented generation (RAG) grounded in your own data
- Evaluation harnesses and regression testing for non-deterministic systems
- Guardrails, safety controls, and human-in-the-loop checkpoints
- LLM integration with your existing systems and APIs
- Observability and tracing for agents in production
- Model selection and cost/latency optimisation
What You Get
Outcomes we hold ourselves to — not activity, results.
- Agents that move from proof-of-concept to production, not another stalled pilot
- Measurable quality — every change tested against an evaluation suite before release
- Safe autonomy: agents that take real actions with the right guardrails around them
- Systems your team can operate, because the tracing and monitoring were built in
Tools & Technologies
We stay model- and platform-agnostic — the right tool changes fast; the engineering standard doesn’t.
- Claude API
- OpenAI API
- Model Context Protocol (MCP)
- LangChain / LangGraph
- Vercel AI SDK
- Python
- TypeScript
- Azure
Senior-led
A principal engineer accountable from first spec to production.
Built for production
A decade of shipping systems that could not afford to fail.
AI-augmented delivery
Specs, coding agents, and automated review on every build.
Let’s talk about your Agentic AI Engineering work.
Tell us what you’re trying to ship. We’ll tell you, honestly, how we’d approach it.