What's 'agentic' and how is it different from a chatbot?

An agent is an LLM-powered system that chooses actions to complete a task autonomously. It calls tools, queries data, writes files, sends messages. A chatbot answers. The engineering challenge is two-fold: constrain the action space tightly enough that the agent stays reliable, and instrument it well enough that you can tell when it isn't.

How do you prevent hallucinations and bad outputs?

Three layers. Grounding (retrieval, tool calls, structured outputs). Guardrails (schema validation, allow-lists, rule engines for high-stakes outputs). Evals (golden datasets, regression tests, human review for edge cases). We ship with all three. We also build a rollback path so you can recover when the model changes behaviour after a provider update.

How do you manage LLM costs in production?

We treat cost as a first-class engineering constraint. We route tasks to the cheapest model that passes evals, cache aggressively, batch when latency permits, and set per-tenant budget caps with circuit breakers. Every production agent has a cost dashboard. Unusual spend triggers alerts before the invoice does.

Which models do you recommend?

Depends on the task. We benchmark 2 to 3 candidates against your eval set before committing. For complex reasoning we typically start with Claude Opus or GPT-5. For high-volume classification we drop to Haiku, Sonnet, or a fine-tuned open-weights model. We stay model-agnostic by design so the architecture won't lock you to one provider.

Can you work with our existing data and APIs?

Yes. That's usually where the value sits. We build retrieval pipelines over your knowledge base, wire agents to your internal APIs with scoped credentials, and respect your permission model so the agent can't see data the user couldn't. Zero-trust by default.

How do you handle safety and abuse?

Input filtering, prompt-injection defences, output moderation, rate limits, and audit logging. For customer-facing agents we run a red-team pass before go-live. For internal copilots we stage rollouts by user cohort. We fix safety issues before the next release instead of documenting them as known issues.

How does this differ from the Custom Software capability?

Custom software is the broader discipline. Backends, data, integrations, internal tools. Agentic AI is a specialisation within it, with its own evaluation practice, cost economics, and failure modes. Many engagements combine both. An agent is usually one feature in a larger custom-software system.

Capability · Agentic AI development← All services

Agentic AI
that ships work.

Autonomous agents, multi-agent systems, retrieval pipelines, and tool-using LLM stacks. We wire in evals, guardrails, and cost controls before launch. Built to run in production, not on a keynote stage.

Eval-first

engineering practice

Tool-aware

agent orchestration

Cost-capped

LLM pipelines

What this means at Enigmatix

Agents that do work, not flashy demos.

Most agentic-AI work in the wild is a prompt, a chain, and a deployment prayer. We build it differently. Every agent we ship runs through eval harnesses against real task traces. Each run carries a cost budget, tool calls hit a guardrail, and a human-in-the-loop escape hatch catches the cases the model gets wrong.

We've built production agents for customer support triage, document processing, research synthesis, code review, and internal-ops copilots. The pattern repeats. Measure first. Scope the action space tightly. Instrument everything. Ship behind feature flags with a rollback path.

Representative work

Shipped under this discipline.

A sample of projects where this capability was load-bearing. We omit client names by default and share them under NDA when you want to dig into a specific engagement.

What we build

Six shapes of agentic systems.

Most engagements are a mix of two or three of these. We scope the exact cut during discovery and write the evaluation plan into the statement of work.

/01

Task-specific agents.

Single-purpose agents that complete a bounded workflow: triage a ticket, draft a reply, extract fields from a document. Scoped action spaces, narrow tools, high reliability.

/02

Multi-agent orchestration.

Planner/worker topologies, DAG-driven pipelines, and handoff protocols between specialised agents. We build on AutoGen, CrewAI, LangGraph, or bespoke orchestrators when the frameworks don't fit.

/03

RAG and retrieval pipelines.

Hybrid retrieval, re-ranking, chunking strategies, and freshness controls. We evaluate on grounding and citation quality, not just vibes.

/04

Tool-using copilots.

Agents that call your APIs, query your databases, and drive your internal tools. We add permission boundaries, audit trails, and cost caps per session.

/05

Eval harnesses and observability.

Production-grade evals with golden datasets, regression tracking, and prompt-change review gates. We add tracing, cost dashboards, and alerting on drift.

/06

Fine-tuning and distillation.

When prompting plateaus, we move to LoRA, QLoRA, full fine-tunes, or distillation into cheaper models. We measure before and after rather than claim the improvement.

Tech we work in

A pragmatic agentic stack.

We pick models and frameworks to fit the task and budget, not the trend cycle. The tools our engineers ship with most often:

/01

Models

Claude Opus / Sonnet / HaikuGPT-5 / 4oGemini 2.xLlama 3.xMistralQwenOn-device Phi

/02

Agent frameworks

LangGraphLangChainAutoGenCrewAIClaude Agent SDKOpenAI Agents SDKBespoke orchestrators

/03

Retrieval & vectors

pgvectorPineconeWeaviateQdrantElasticsearchBM25 hybridCohere rerank

/04

Evals & observability

BraintrustLangSmithPhoenix (Arize)Weights & BiasesBespoke eval harnessesOpenTelemetry

/05

Runtime & infra

PythonTypeScriptModalTemporalRayAWS BedrockVercel AI

How to engage

Pick the engagement model
that fits the commitment.

The same agentic ai development work ships under any of these three commercial shapes. The difference is in how you hold us accountable and how you scale up or down.

Dedicated teams

A cross-functional pod of engineers, QA, DevOps, and design, working only on your product.

Offshore development centre

A managed engineering arm in our region. Your roadmap and IP, our ops and infra.

Staff augmentation

Vetted senior engineers who plug into your team and backlog.

Frequently asked

Common questions, answered.

Can't find what you're looking for? Email discovery@enigmatixglobal.com and we'll reply within one working day.

Book a 30-min call

An agent is an LLM-powered system that chooses actions to complete a task autonomously. It calls tools, queries data, writes files, sends messages. A chatbot answers. The engineering challenge is two-fold: constrain the action space tightly enough that the agent stays reliable, and instrument it well enough that you can tell when it isn't.

Trusted by our clients

What partners
tell us.

Most clients renew for a second engagement. The ones who don't usually hire someone from our team to run the project in-house.

We are a mental health charity and not tech minded. We needed to improve our patient registration system but wanted a company that we can relate to. Hassan took the time to meet with me and patiently went through our process to find a personalised solution for us. Hassan's team did a great job streamlining our process and connected our registration system to our patient management software. Whenever we have a glitch, they resolve this quickly. Cloudini provides a personal and efficient service and we have adopted them to be our IT provider.

Tien Kuei

CEO · Power to Live Foundation

I am writing to wholeheartedly recommend Cloudini as a premier bespoke software development agency. Our journey with them has been nothing short of exemplary, far exceeding our initial expectations. While many agencies can develop based on strict specifications, Cloudini demonstrated an incredible commitment to our project that was truly unparalleled.

Robert McPherson

Managing Director · RBM Global Ltd

Dragonfly has been working with the team at Cloudini for several years now on building out our web-based platform and APIs. One of their strengths is quickly building out a multi-disciplinary team to work on the development and delivery of features with different technical requirements. Their support has allowed us to accelerate our development work without building an internal team. They have also worked seamlessly with other third parties both during the acquisition of Dragonfly and development of our mobile app. Cloudini has been instrumental in bringing our ideas to life and providing guidance to help us make the best technical decisions. I would recommend getting in touch with them if you're looking for support.

Rebecca Palser

COO · Dragonfly

Hassan and the Cloudini team have been indispensable partners as we rebuilt our client-facing applications into a single platform. As well as being exceptional technically, they consistently invest the time to understand the product and suggest how it can be enhanced and improved.

David Claridge

CEO · Dragonfly

Cloudini did some great work for Sainsbury's, bridging complex interactions between some older less flexible systems, some rigid vendor solutions and the new systems we were building. The software Cloudini developers wrote stood up well, long beyond the interim scope of it needing to work for 12-18m and was still in active use 5 years after its creation to good effect, since it was part of mission critical systems.

Fraser Pearce

Senior Engineering Manager · Sainsbury's

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Book a 30-min call discovery@enigmatixglobal.com

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Agentic AI
that ships work.

Agents that do work, not flashy demos.

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