Production AI agents for businesses drowning in
manual work.

The problem

Your team is the bottleneck. Reports are late.

• Reports that take days to pull
• Spreadsheets sprawled across the org
• Workflows that need a human babysitter
• CRMs full of stale data
• "Ask Sarah" being your single point of failure
• Engineering team booked solid on revenue work

What is an agentic ops system?

Real software with a reasoning layer.

Three things real software does, plus one new thing. Most automation breaks the moment something doesn’t fit the schema. Agents handle the exceptions.

• Reads

  Your data across CRM, ERP, warehouse, SaaS, sheets — wherever it lives.

• Manipulates

  Records, fields, statuses. Triggers downstream workflows. Updates source-of-truth systems.

• Coordinates

  Multi-step processes across systems. Hands work between agents and humans.

• Reasons

  Through ambiguity using LLMs — the "it depends" cases that break Zapier.

What we build

Concrete examples.

• Executive reporting agent

  "Brief me on Q-to-date pipeline by segment, flag anomalies vs. last quarter, Slack me a 5-bullet summary every Monday."

• RevOps workflow agent

  "Watch HubSpot, enrich every new lead, score against ICP, route to the right AE, log activity, notify the team."

• Finance operations agent

  "Pull QuickBooks + Stripe + bank, reconcile, flag anything off, draft the weekly cash report."

• Internal knowledge agent

  "Let our team ask questions across Notion, Slack, and our SOPs in plain English."

• Order operations agent

  "Watch Shopify orders, flag fulfillment risk, open Linear tickets, ping the ops Slack."

For more patterns across sales, finance, support, operations, HR, knowledge work, and voice, see 15 AI agent examples with controls and KPIs. When one matches your workflow, use the seven-step AI agent development process to take it from definition to a controlled production launch.

How we’re different

Owned code. Real reasoning. Days, not months.

The three packages

Choose the smallest scope that covers the operating requirements.

Need to separate implementation work from model usage, hosting, monitoring, maintenance, and human review? Use the AI agent cost guide and estimation worksheet before comparing packages.

FAQ

Last-mile objections.

What we ship

Real code. Owned by you.

Every engagement ships to your repo. Typed, tested, documented, with a clean module layout. Below is a representative slice from a recent build.

# agents/exec_brief.py
from ctb import Agent, sources, channels

@Agent.scheduled("0 7 * * *")  # 7am daily
def exec_brief(ctx):
    pipeline = sources.snowflake.query(ctx.sql.weekly_pipeline)
    revenue  = sources.stripe.last_n_days(7)
    deltas   = ctx.compare(pipeline, baseline="trailing_4w")

    flagged  = [d for d in deltas if d.sigma > 2]

    summary = ctx.llm.narrate(
        prompt=ctx.prompts.exec_brief,
        data={"pipeline": pipeline, "revenue": revenue, "flagged": flagged},
    )

    channels.slack.post("#leadership", summary, thread_handlers=[ctx.followup])
    return ctx.log(metric="exec_brief.posted")

Stack agnostic. We ship in the language and infrastructure that fits your team — Python, TypeScript, your cloud.