AI Systems for AI Agent DevelopmentAI Systems for

Automation runs a fixed script and calls an LLM at a few predetermined steps — predictable when inputs match the template, fragile when they vary, and easy to debug because the control flow is hard-coded. Think Zapier routing a support ticket to a queue based on the subject line. An agent has working memory across turns, structured tool access with permission boundaries, and a control loop that decides what to do next — it can recover from a failed tool call, ask for help when its confidence drops below threshold, and improve as you grow the evaluation set. Think an agent that reads the full ticket, checks account history, attempts the diagnostic, drafts the reply, and routes to a human only when it should. Automation is a script with LLM calls. An agent is a system that owns the workflow.

Three layers, in production from day one. First, schema-validated structured output: every model response goes through a strict JSON schema check so malformed responses fail loudly instead of leaking downstream as bad data. Second, an evaluation harness that runs the agent against a held-out test set on every deploy — regressions block the merge before the release ever reaches users. We grow that test set every time the agent makes a mistake in production, so the system gets more robust over time, not less. Third, a human-in-the-loop review path on high-stakes actions until the agent earns autonomy on a given workflow. Layered on top: structured logging of every decision, model invocation, and tool call so when something does go wrong, we can replay the exact sequence and patch the specific failure mode quickly instead of guessing.

We pick per project, not per fashion. Most builds use Claude or GPT-class models for the reasoning core because they handle multi-step decisions and tool use the most reliably right now — and we lean toward Claude when the agent has to follow a long list of constraints without drifting. Smaller open-source models like Llama and Mistral handle narrow tasks where latency or cost matters more than raw capability — classification, intent routing, summarization, anything that does not need frontier reasoning. Frameworks: LangGraph when the agent needs stateful multi-step orchestration with branching logic, CrewAI when role-based multi-agent collaboration earns its keep, and pure-code orchestration when either framework would add latency or debugging overhead without earning it back. Model selection criteria: task decomposability, latency budget, context window needs, and the cost of being wrong. We optimize for what survives six months in production, not what trended on Twitter last week.

You own everything from day one. Code lives in your GitHub organization, not ours. Models run on your accounts — your Anthropic API key, your OpenAI key, your AWS, your data. We do not gatekeep deployments, we do not bill per query, we do not host anything you cannot revoke in an hour. A clean handoff means a documented architecture, runbooks for the production checkpoints, the evaluation harness we used during the build, and the model-account transfer paperwork — not a tribal-knowledge dump after a Slack farewell. Most clients keep us on retainer for ongoing iteration, new integrations, and the periodic eval runs that catch model regressions before users do. Some bring it fully in-house in six to twelve months once they have hired the right engineer. Both shapes are fine — we structure the engagement to support either path from the first commit.

AI development services is the broader category that covers everything from data and model work through deployment: data engineering for AI workloads, model selection and fine-tuning, retrieval-augmented generation systems, embedding pipelines, evaluation harnesses, MLOps and observability, and integrating AI features into existing applications. AI agent development is one specific kind of AI development service — building autonomous systems that decide what to do next and call tools to do it. Many of our engagements blend the two. A client comes in wanting an agent, and the first six weeks turn out to be data plumbing and an evaluation set, because the agent is only as good as what it has to reason over. Other clients want classic AI development without the agent shape — a classifier, a search system, a summarization pipeline — and those are equally good projects. When you book a strategy call we will tell you which shape your work actually wants, including telling you it is not an agent if it is not.

A multi-agent system has more than one agent in the loop, each with its own scope, tools, and sometimes its own underlying model. A "research" agent gathers; an "analysis" agent reasons; a "review" agent checks the output before it goes anywhere. The parent or orchestrator agent decides which subagent to call and combines results. The trade-off is real: multi-agent systems are more powerful and more complex than single-agent systems. They are the right shape when the work decomposes cleanly into specialized scopes that are easier to test in isolation than as one big prompt, when different steps benefit from different model choices (a frontier model for hard reasoning, a smaller model for high-volume cheap steps), or when the system needs parallel execution rather than a sequential chain. They are the wrong shape — and we will tell you so — when a single well-designed agent could do the same work with less debugging surface, less latency, and lower model cost. Most production agents we ship are single-agent systems. The multi-agent shape earns its complexity when there is a real reason for the boundaries.