Custom AI Agent Development
We design, build, and operate autonomous AI agents that perceive context, reason through multi-step problems, and take real action through your existing tools and APIs — not scripted chatbots, but software that decides what to do next.
What an AI agent actually does
Most "AI features" bolted onto software are a single LLM call wrapped in a UI: prompt in, text out. An agent is different. It holds a goal, breaks that goal into steps, calls tools or APIs to gather information or take action, evaluates the result, and decides what to do next — looping until the goal is met or it needs a human. That loop is what lets an agent handle a support ticket end-to-end, process an inbound RFQ, or reconcile a document against a database, instead of just answering one question at a time.
What we build
LLM-powered reasoning engines. The core decision loop — prompt design, planning strategy (single-shot, ReAct-style, or multi-step plan-and-execute), and the evaluation logic that checks whether an action actually succeeded before the agent moves on.
Multi-agent orchestration. For workflows too broad for one agent to own reliably, we split the work across specialist agents — an intake agent, a research agent, a drafting agent — coordinated by a controller that routes tasks, passes context, and resolves conflicting outputs.
Tool-use and API integration. Agents are only as useful as what they can touch. We wire agents into your CRM, ticketing system, ERP, payment processor, internal databases, or any REST/GraphQL API, with permission scoping so the agent can only call what it's supposed to.
Memory and context management. Short-term working memory for the current task, long-term memory for facts that should persist across sessions (customer preferences, prior decisions), and retrieval pipelines so the agent can pull relevant context from large document sets without blowing past context-window limits.
How we build it
- Workflow mapping. We shadow the process the agent is meant to replace or augment, and write down every decision point, exception, and escalation path a human currently handles.
- Architecture design. We choose single-agent vs. multi-agent, pick the reasoning strategy, and define the tool and memory layer before writing production code.
- Guardrails and evaluation. Every agent ships with an action allowlist, confirmation steps before irreversible actions, and an automated evaluation suite that runs before each deployment.
- Deployment and monitoring. Agents go live behind human-in-the-loop approval by default; we widen autonomy as monitoring data shows the agent is performing reliably.
Where this fits
The agent pattern applies anywhere a person currently follows a repeatable-but-not-trivial process: customer-facing support and billing questions, internal operations bots that triage and route work, research analysts that synthesize information from multiple sources, and document-heavy workflows like RFQ intake and quotation drafting. You can try working versions of several of these — a support agent, a document processor, a research analyst, and an RFQ-to-quotation pipeline — on our live demo platform before committing to a build.
Technology approach
We're model-agnostic by design. Engagements typically run on Anthropic Claude or OpenAI's GPT models, with open-weight models used where data residency or per-call cost makes a hosted API impractical. The tool layer, memory system, and evaluation harness are built independently of the underlying model, so a provider switch later is a configuration change, not a rewrite.
How engagements are scoped
Most agent builds start as a fixed-scope pilot on one workflow — usually four to eight weeks — so you can see real output against real data before committing to a wider rollout. Once the pilot is validated, we either hand off a documented, monitored system to your team or continue as the ongoing engineering partner responsible for uptime, model updates, and expanding the agent's scope. Pricing depends on workflow complexity, the number of systems the agent needs to integrate with, and whether the engagement is fixed-scope or ongoing — we'll give you a concrete range after the first scoping call rather than a generic rate card.
Frequently asked questions
What's the difference between an AI agent and a chatbot?
A chatbot follows a scripted conversation tree or answers single questions from a knowledge base, one exchange at a time. An AI agent perceives context, plans multi-step actions, calls external tools and APIs, and adapts its next move based on what it learns along the way — often chaining several tool calls together before it ever responds to the user. A support chatbot can tell a customer their order status; a support agent can look up the order, check the carrier API, issue a refund if policy allows it, and escalate to a human only when the case falls outside its authority — all without a human writing the conversation flow in advance. The practical difference shows up in scope: a chatbot handles the questions its designers anticipated, while an agent can handle novel combinations of steps because it reasons about the goal, not a pre-written script.
Which LLM providers do you build agents on?
We are model-agnostic, and the choice depends on what the agent actually needs to do. Most engagements run on Anthropic Claude or OpenAI's GPT models, selected per task based on latency requirements, cost per call at expected volume, and how much multi-step reasoning the workflow demands — a simple classification step doesn't need the same model as a research agent synthesizing multiple sources. We've also deployed agents on open-weight models where data residency rules, air-gapped environments, or high call volume make a hosted API impractical or too expensive. Switching providers later — or routing different steps of the same agent to different models — is a configuration change, not a rebuild, since tools, memory, and evaluation are built independently of the underlying model.
How do you prevent an AI agent from taking the wrong action?
Every agent we ship has explicit guardrails rather than relying on the model to behave well by default: a defined action allowlist so it can only call the tools it's meant to use, confirmation steps before irreversible actions like refunds, deletions, or sends, and automated evaluation suites — built from real and adversarial test cases — that run before every deployment and on every prompt or tool change afterward. High-stakes actions route through human-in-the-loop approval by default until the agent has a track record of correct decisions in production; we widen its autonomy gradually as monitoring data shows it's warranted. Every action is logged with the reasoning that led to it, so if something does go wrong, we can trace exactly why.
Ready to scope your agent?
Tell us the workflow you want to hand off, and we'll map what's feasible in a first build.
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