Where agents fit
- Internal automation: tickets, code review, runbooks, knowledge work
- Developer productivity with Claude Code and custom subagents
- Customer-facing flows where review and rollback are designed in
Agentic Workflows
Agents that do real work — not demos.
We design and ship agentic workflows that integrate with your existing systems, follow your guardrails, and produce measurable outcomes.
Most teams have run an agent prototype by now. Far fewer have one in production handling work that humans used to do. The gap is engineering: tool surfaces, context management, evaluation, observability, and the discipline to know when an agent is the wrong answer.
We help you pick the right workflows to automate, build them with Claude Code, MCP servers, and custom tooling, and operate them reliably alongside your team.
How we build them
- MCP servers that expose your tools and data to agents safely
- Skills, hooks, and permission policies tuned to your environment
- Evaluation, observability, and cost controls from day one
How we engage
- Discovery sprint: identify two or three high-value agentic workflows
- Hands-on build alongside your engineers
- Production hardening: guardrails, audit, on-call patterns
- Knowledge transfer so your team owns it after we leave
FAQ
Common questions about agentic workflows
What's the difference between an AI assistant and an agentic workflow?
+
An assistant responds to prompts. An agentic workflow takes action on its own — it reads context, decides what to do, calls tools, and produces an outcome. The shift is from text generation to work completion.
When is an agent the wrong solution?
+
When the task is fully deterministic, latency-sensitive, or has no tolerance for retries. A small script or a workflow engine will be cheaper, faster, and more predictable. Agents earn their keep where the work involves judgement, varied input, or long context.
What does production-readiness mean for an agent?
+
Three things: bounded permissions (it cannot do anything you didn’t authorise), an evaluation harness (you can tell when it regresses), and observability (you can debug a single run end to end). Without these, you have a demo, not a system.
How do MCP servers fit in?
+
MCP (Model Context Protocol) is how an agent reaches your tools and data — your database, ticketing system, internal APIs — through a standard interface. We build MCP servers with permissions and audit baked in, so an agent gets just the right surface area, not more.
How long does a typical agentic engagement take?
+
A discovery sprint runs one to two weeks. A first production agent typically takes four to eight weeks. Hardening, evaluation, and team handover usually take a similar amount of time on top, depending on regulatory exposure.