Service 01

Agentic workflows

We build agents that carry a whole process end to end — reading the request, calling your systems, checking their own work and handing a human only the decisions that genuinely need one.

What it includes

Task decomposition you can read

Every agent runs against an explicit plan, so an operations lead can see which step ran, what it called and why it stopped.

Tool calling into your real systems

ERP, CRM, ticketing, email and internal APIs are wired as typed tools with permission scoping per agent role.

Self-verification before hand-off

A second pass checks the output against your rules and the source data, and routes anything below the confidence threshold to review.

Human approval gates

Payments, contract changes and customer-facing commitments wait for a named approver, with the full context on one screen.

Deterministic fallbacks

Regulated steps run on plain code, not a model, so an audit can reproduce the result exactly.

Full run history

Every run stores inputs, tool calls, outputs and cost, so you can replay a decision months later.

What you receive

—Workflow map with the agent boundary marked
—Agent implementation with tool permissions
—Evaluation suite and confidence thresholds
—Approval and exception queues
—Runbook and monitoring dashboard

Typical stack

Python / TypeScript
LangGraph-style orchestration
Postgres
Queue workers
OpenTelemetry

·Task decomposition and tool-calling design
·Human-in-the-loop approval gates
·Deterministic fallbacks for regulated steps

Questions we are asked

How is this different from a chatbot?: A chatbot answers. An agent acts: it reads a request, calls your systems, changes records and closes the loop. The conversation is optional and often there is no chat interface at all.
What stops an agent from doing something wrong?: Three things: scoped tool permissions, so it can only touch what it needs; approval gates on anything with financial or legal weight; and a verification pass that fails closed into a human queue.
How long before the first agent is live?: A single workflow typically reaches a working prototype in three to four weeks and production in eight to twelve, depending on how ready the integrations are.
Which models do you use?: Whichever is best for the step and the budget, and we benchmark them on your data before committing. The orchestration layer is model-agnostic so you are not locked to one provider.
Can it run on our own infrastructure?: Yes. We deploy into your cloud account or on-premises, and we can run open-weight models where data residency requires it.

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