AI Agents & Automation

Most agent demos are a loop and a system prompt. That is the easy 20%. Cravings builds the other 80%: the evals, the guardrails, the observability, and the day-two operations that make an agent something you can actually put in front of paying customers.

What we ship

• Triage and routing agents — read tickets, classify intent, draft responses, escalate cleanly.
• Internal copilots — slash-command bots, Slack assistants, IDE plug-ins for engineering and ops teams.
• Document and data agents — long-context extraction, structured outputs, citations you can trust.
• Voice and multimodal flows — telephony, image understanding, hand-offs to humans.
• Tool-using agents — read-and-write access to the systems your team already runs.

How we work

We start with the eval set, not the prompt. The first deliverable on every agent engagement is a written specification of what “good” looks like — graded rubrics, golden traces, red-team prompts. The agent is what passes those evals. The prompt is an implementation detail.

From there we build in two-week iterations behind feature flags, instrumented with traces from the first commit. By the time we hand over, your on-call rotation has a runbook and a Grafana board.

Stacks we like

Anthropic and OpenAI SDKs first. We are happy in TypeScript, Python, or PHP — whichever your team already runs. For orchestration we lean toward small composable pieces (queues, workflows, scheduled jobs) over heavyweight frameworks.

Typical engagement

Six to twelve weeks from kickoff to a production agent. The first two weeks are a paid diagnostic; weeks three to six are the working prototype; weeks seven onwards are hardening and rollout.