Custom AI agents that decide, call tools, and complete multi-step work — built on Anthropic Claude, OpenAI GPT, and Google Gemini. Our own sales agent runs on this site: it qualifies leads, asks the right questions, and emails our team when it finds an opportunity. The same patterns transfer to your operations.
Agent capability in 2026 is genuinely uneven across providers. Claude leads on long, multi-step reasoning and tool use. GPT has the most mature ecosystem for function-calling and voice. Gemini wins when the agent must read a million-token corpus or run high volume cheaply. We pick per workload — and we will tell you when a deterministic pipeline beats an agent for part of the flow.
Strongest model for agentic work
Claude 4 family with extended thinking, native sub-agents, and first-class Model Context Protocol (MCP) for tool integration. Stays inside instructions under long, multi-turn workflows — what you want when an agent is making real decisions on your behalf.
Mature tooling and structured outputs
GPT-4o and the latest GPT family, the Assistants API for stateful agents, and the Realtime API when voice or low-latency interaction matters. Reliable function calling, predictable JSON outputs, and a deep ecosystem of integrations.
Long context and high-volume economics
Gemini Pro for agents that must read entire codebases, document sets, or transcripts in a single turn (up to 1M tokens). Native function calling and grounded responses. Strong default when the workload is high-volume and cost-sensitive.
Six agent patterns we ship today. Sales is what is running on this site, so you can see it in action before we quote anything. The rest reuse the same engineering core — tool layer, decision logic, eval harness, observability — adapted to your domain and integrations.
Agents that talk to inbound prospects, ask the right qualifying questions, decide when there is a real opportunity, and hand a curated brief to your team. The agent on this site is the reference implementation — same pattern, your pipeline.
First-line support that classifies tickets, resolves the simple ones with tool calls into your systems, and escalates the rest with full context attached. Frees the human team for the work that actually needs them.
Alert triage, runbook execution, on-call summarisation, and routing into Jira or Linear. Agents that read the runbook, take the safe steps automatically, and ping a human when the call is genuinely judgment-shaped.
Multi-step research over public sources, your CRM, or internal docs — gather, summarise, structure, and produce a brief. Useful for competitor analysis, prospect research, and recurring market reviews.
Read incoming documents, extract structured fields, classify, and route. Invoices, contracts, application forms, and inbound paperwork pipelines — agents replace the brittle OCR-plus-regex stack with something that handles edge cases.
Conversational agents that guide a customer or new employee through a structured flow with tool-calls into your backend — provisioning accounts, scheduling sessions, collecting compliance data, completing each step before moving on.
Most "AI agent dev" gigs end at a working prompt and a couple of tool definitions. Ours start there. Below is what we operate around our own production agent — tool layer, decision logic, evaluation, human-in-the-loop, cost guardrails, observability — and what you inherit on day one of your engagement.
Swap between Claude, GPT, and Gemini per workload without rewriting the agent. Pricing or capability shifts do not lock you in.
First-class tool schemas, including Model Context Protocol servers — the open standard for connecting agents to your databases, APIs, and SaaS. Reusable, typed, testable.
Explicit routing between sub-tasks, retries, and escalations. Agents that know when to act, when to ask, and when to hand off — with the logic visible, not buried in a prompt.
Short-term context across turns and longer-term memory where the use case calls for it. Scoped, auditable, and clearable per session or per user.
Agents fail differently from chatbots — they make wrong decisions, not wrong sentences. We ship a behaviour eval harness with each engagement: trajectory tests, tool-call assertions, and red-team scenarios run on every change.
Configurable approval gates for high-impact actions: send the email, run the migration, refund the customer. The agent prepares the action; a human signs off. Risk lives where you put it.
Max iterations, max tokens per turn, per-session cost ceilings, and circuit breakers. Runaway agent loops cost a fixed budget, not an open one.
Structured logs of every decision, every tool call, and every model response, with request IDs and timing. Drops into Datadog, Loki, Langfuse, or whichever observability stack you already run.
Six steps. Discovery starts with a feasibility check — what decisions you want the agent to make, what tools it needs, and where the failure cases live. We will tell you in that call if a deterministic workflow or a simple integration is the better answer (sometimes it is), and we always run evaluation passes before any agent goes live to real traffic.
Send a brief through the contact form. We reply within one business day to set up a discovery call. You will leave the call with a model recommendation, a tool inventory, an eval plan — and a scoped proposal. Or a clear no, if an agent is not the right answer.
