AI agents and tool use
Agents that call your APIs and internal systems through validated, allowlisted tools, with confirmation before anything sensitive.
Home / AI Agent Development Services
AI Agent Development Services
AI agent development services for teams that want more than a chatbot: agents that read your real data, call your real tools, and run work end to end without someone babysitting them.
RAG over your own data
Retrieval-augmented generation across your documents and databases: chunking, embeddings, hybrid search, and reranking.
AI automation services
Workflow automation that connects your CRM, inbox, and databases, built so failures are visible instead of silent.
What AI agent development actually involves
Most AI agent projects do not fail at the model. They fail at everything around it: unvalidated tool arguments, prompts that drift, costs nobody tracks, and no way to tell whether the output got better or worse after a change.
- Agent design: planning, tool selection, retries, timeouts, and cancellation
- RAG pipelines: chunking strategy, embeddings, vector search, reranking, and freshness
- MCP (Model Context Protocol) servers so models reach internal tools safely
- Evaluation sets so a prompt or model change can be measured, not guessed at
- Guardrails, human confirmation steps, and audit trails for sensitive actions
- Token and cost control, latency budgets, caching, and monitoring in production
The deliverable is a running system on your infrastructure with documentation, not a notebook or a demo video.
Where AI agents are worth it
Agents pay off when a task is repetitive, rule-heavy, and currently done by a person copying information between systems. Support triage, order and booking workflows, document extraction, research summaries, and internal question answering over company data are all common fits.
They are usually not worth it when the task is rare, needs judgement that nobody can describe, or when a plain script would do the same job more cheaply and more predictably. I will say so if that is the case.
Automation platforms I work with
Some workflows belong in code, and some belong in an automation platform with a clear visual trail. I build both, and connect them to the same agent layer.
- n8n agentic AI automation for self-hosted pipelines
- Zapier agentic AI automation for connecting SaaS tools quickly
- Make.com automation for agentic AI for scenario-based workflows
- AI agent apps in React Native when the agent lives in a mobile product
Why work with me
I am a senior engineer with 9+ years shipping production software, not an AI-only consultant. That matters because an agent is only useful once it is wired into a real product, a real backend, and a real release process.
- Apple featured one of my React Native apps on their official iPhone 15 page
- Author of react-native-compressor, downloaded 220,000+ times a week on npm
- 25,000+ reputation on Stack Overflow across 261 answers
- Production experience across mobile, Node.js microservices, Kafka, and Kubernetes
Related pages
FAQ
What are AI agent development services?
Designing and building systems where a language model plans a task, calls real tools or APIs to carry it out, checks the result, and reports back, instead of only replying with text.
How is an AI agent different from a chatbot?
A chatbot answers. An agent acts: it selects tools, performs steps, handles failures and retries, and can be stopped or asked to confirm before doing anything sensitive.
Can you add an AI agent to an existing product?
Yes. That is the most common request. The agent layer sits alongside your current backend and is introduced behind a feature flag so you can roll it back.
Do you use OpenAI or Anthropic models?
Both, depending on cost, latency, and the task. The integration is kept provider-agnostic so a model can be swapped without rewriting the system.
How do you keep AI costs under control?
Token budgets per request, caching, smaller models for routine steps, and monitoring so spend is visible before it becomes a surprise.
My Notes on AI Agent Development Services
I wrote this page for people who want a practical view of ai agent development services before they make an engineering decision or ask for implementation help.
My preference is to start with the product constraint, then choose the technical approach. A mobile app usually has competing pressures: delivery speed, app size, startup time, offline behavior, platform-specific details, analytics, release risk, and the cost of maintaining the code after the first version ships. Good React Native work keeps those pressures visible instead of hiding them behind library choices.
When I review a codebase or plan a new build, I look for the parts that will create the most operational risk: slow screens, unclear state ownership, fragile navigation, native modules without a release plan, missing test coverage, oversized images, and app-store workflows that depend on manual steps. Fixing those problems early is usually cheaper than trying to recover after users start reporting crashes or performance issues.
That is also why the pages on this site link to each other. Architecture affects performance, testing affects release confidence, Expo choices affect native integration, and component-level decisions can show up later as accessibility, debugging, or maintenance problems. The goal is not to make the app look technically impressive. The goal is to make it stable, understandable, and easy for a real team to keep improving.