The specialist AI agent development consultancy for enterprises. We design, build and ship autonomous and multi-agent systems for production, with the monitoring, retries and fallback workflows that real operations demand. Trusted by Google, Microsoft and Stability AI, delivering AI agents since 2013.
Tell us about your AI agent project, single-agent prototype, multi-agent orchestration, RAG knowledge agent or full enterprise deployment, and we'll tailor an approach. Typically two to four weeks from first call to kick-off.
AI agent development services turn a large language model into a reliable, tool-using software system that performs real work. That means custom AI agent development, tool integration, multi-agent orchestration, evaluation, guardrails, observability and the fallback workflows that enterprise production demands. Winder.AI delivers end-to-end AI agent development as one engagement, strategy, custom agent build, multi-agent orchestration and ongoing operations, by the same senior engineers who shipped production AI for Temple University, Google, Microsoft and Stability AI. We are framework-agnostic across LangChain, LangGraph, PydanticAI, CrewAI, AutoGen and the OpenAI Agents SDK, and we ship working agents, not slide decks.
2026 update. Frontier model providers (OpenAI, Anthropic, Google) now expose first-class agent loops, structured tool calling and built-in evals, so the framework decision matters less than it did in 2024. The hard parts in 2026 are the same as they were in 2013: evaluation, guardrails, observability, retries and fallback workflows. We pick the lightest stack that meets your reliability bar, often native tool use plus a thin orchestration layer rather than a heavyweight framework.
| Provider type | What they deliver | Best for | Main weakness |
|---|---|---|---|
| Big-4 / global strategy consultancy | Agentic AI strategy decks, roadmaps, large delivery teams | Multi-year transformation programmes | Hands-on agent engineering offshored or thinly staffed, weak on production reliability |
| Generalist AI agency | Broad AI capability with agent work as one offering | Single-LLM chatbot prototypes | Shallow agent bench, weak on multi-agent orchestration, evaluation and guardrails |
| OpenAI / Anthropic solutions team | Reference implementations on the vendor's models and tooling | Adopting a single model provider | Lock-in by design, weak on open-source models, multi-cloud and on-prem |
| No-code agent builder vendor | Their drag-and-drop platform, plus implementation services around it | Internal proofs of concept with simple workflows | Hits a ceiling fast on complex tool use, custom integrations, evaluation and observability |
| In-house build (your team) | An agent built by your existing engineers, on your stack, with your domain context | Long-term ownership when you already have a senior ML or platform team with spare capacity | Learning curve on agent frameworks, evaluation and guardrails delays first production agent by 6 to 12 months |
| Specialist AI agent consultancy (Winder.AI) | AI agent strategy, custom agent development, multi-agent orchestration and ongoing operations, delivered by senior AI engineers | Enterprises that need production AI agents with monitoring, retries and fallback workflows, multi-cloud, framework-agnostic | Boutique scale, not designed for 100-seat staff augmentation |
Winder.AI is the AI agent development partner for enterprises that need autonomous systems to run in production, not in a notebook. Our AI agent services span strategy and architecture, custom agent build, and multi-agent orchestration, the full lifecycle, by senior engineers who have shipped autonomous AI since 2013.
We sought AI engineering experts that could quickly learn our day-to-day scientific legal mapping processes enough to develop a tool to make our work more efficient. Winder.AI dug into our day-to-day workflow to thoroughly understand the value of an AI Assistant for scientific legal mapping, which is a critical process to the field of legal epidemiology.
A decade-plus of autonomous AI in production, framework-agnostic delivery and a senior engineering bench, not a sales layer.
Production AI agents delivered across legal, finance, technology, manufacturing, energy and regulated public services.
Production AI agents are the difference between a flashy demo and a reliable business system. Winder.AI delivers AI agent solutions as discrete service lines, from focused tool-calling agents through to multi-agent orchestration platforms, so you can engage at any stage of your agentic AI roadmap:
We cover the full agentic AI stack across the major frameworks, LangChain, LangGraph, PydanticAI, CrewAI and AutoGen, and the operational disciplines that turn an LLM prototype into a reliable autonomous system:
A complete AI agent built as an explicit LangGraph state graph: extract, validate, self-correct, post. Open source on GitHub, with 100% extraction accuracy on a held-out test set.
The invoice-triage agent demo shows how we build production AI agents for clients: a typed state object, a graph of nodes and conditional edges, a self-correcting extraction loop, bounded retries on transient failures, and a held-out evaluation with exact ground truth (no LLM judge, no judge bias). Every model call goes through one OpenRouter key, and each extraction is content-addressed and cached, so re-running an invoice replays the cached result and never re-bills.
Real numbers from the run on 20 synthetic invoices (Claude Sonnet 4.6, seed 7, temperature 0):
| Metric | Score |
|---|---|
| Field-level extraction accuracy | 100% (1.00) |
| Exact-invoice match rate | 100% (1.00) |
| Post success rate | 100% (1.00) |
| Mean cost per invoice | $0.0081 (1,476 tokens) |
Full source, agent graph, eval harness and committed results: github.com/winderai/winder-demos-working-agent.
Case study
Winder.AI helped Duetto evaluate offline reinforcement learning for dynamic hotel pricing. Over five months, the engagement progressed from behavioural cloning baselines through Implicit Q-Learning experiments on real booking data, revealing where RL outperforms simpler approaches, what data quality prerequisites exist, and how to evaluate pricing agents when ground truth is unavailable.
Case study
Winder.AI built a legal AI assistant for the Center for Public Health Law Research at Temple University, automating the scientific legal mapping work behind its MonQcle platform. In testing, the CPHLR team measured it as nearly 80% accurate at identifying the appropriate legal text for a coding question.
Case study
Our financial client is based in the UK. They specialise in providing services to the finance industry. Their data science team embarked on a project to leverage reinforcement learning within their product offering. Winder.AI, world-leading authors and experts on reinforcement learning, helped them deliver their POC into production. Read on to find out more.
AI Agents
I’ve spent the last few months on Helix-Org, my attempt at rebuilding an organisation as AI agents. The first version had me writing hyper-specific agents in code, each one specialising in a single job. It worked, and it became tiresome: every new job meant another small program to write, test and maintain. Eventually I tried describing one of them in Markdown instead and handing it to a harness. It did the same job. Markdown is now code.
DeepSeek shipped an agent harness in developer preview on 13 August 2026, and it collected 95,386 GitHub stars in about two days, one of the fastest adoption curves GitHub has recorded. The thing I stumbled into has a name, a plugin standard and nine products worth choosing between. We run seven of them. Which one you pick matters less than which layer you need, and no feature grid answers that.
AI
We spent five months with Duetto working out whether reinforcement learning could price hotel rooms better than the heuristics they already had. The algorithm was never the hard part. Nobody can observe what demand would have been at a price the hotel did not charge, so there was nothing to check an answer against, and the measuring instrument had to be built before anything we said about the agent meant much. When we turned on that instrument and looked at it properly, the revenue lift it reported correlated with the error in the demand model underneath it. It had been flattering the agent in proportion to how wrong it was.
Agents built on language models have a smaller version of the same problem, and it arrives the week someone senior asks whether the thing is safe to ship. Until the measuring instrument exists, everything the agent produces is an anecdote.
AI
The agent has been fine for six weeks. Then a customer complains, you open the trace, and every step is green. Nothing timed out, nothing threw an exception, and the summary at the end says the job is done. It is not done.
Agents fail in production in a small number of recognisable ways, and almost none of them are the model being wrong. They call the right tool with the wrong arguments. They run out of context partway through a long task and forget a constraint you gave them at the start. They report success after a step that failed. They loop, and you find out when the bill arrives. Or they answer confidently from data that stopped updating on Tuesday.
Every one of those has an engineering fix, and every fix is code inside your agent loop, not a product you buy.
This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.
Whether you need an AI agent strategy review, a custom tool-calling agent, a multi-agent orchestration platform or ongoing operations for production autonomous systems, talk to the team that has been shipping autonomous AI since 2013.
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