AGENTIC AI AUTOMATION

Agentic AI automation that runs complex workflows end to end.

Agentic AI automation hands a multi-step workflow to AI agents that read the data, decide the next step and act in your tools, from Shopify and your ERP to Gorgias and Slack. BRISTM builds these workflows for commerce teams, with clear rules for what an agent may do alone and what a person must approve. You keep a full audit trail, and your team stops copying data between tabs.

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Agentic AI automation in numbers

  • 20+hrs

    Saved per team, per week

  • 35%

    Faster response times

  • 3×

    More workflows handled per team

  • 24/7

    Automated workflow availability

AGENTIC AI EXPLAINED

What is agentic AI automation?

Agentic AI automation is the use of AI agents to complete a multi-step business workflow towards a goal. Instead of following one fixed script, the agent reads the situation, picks the right tool, checks the result and moves to the next step. It asks a person to approve anything risky, such as a refund or a price change.

Rule-based tools like Zapier, Shopify Flow or RPA bots work well when every case looks the same. They break when an email is vague, a supplier file changes format or an order needs judgement. Agents handle that variation. In practice we often combine both: fixed rules for the simple path, an agent for the exceptions.

Trigger → Fixed action

  1. Trigger
  2. Fixed action

Goal → Plan → Act

  1. Goal
  2. Plan
  3. Use tools
  4. Check data
  5. Decide
  6. Act
  7. Report

Human approval when needed

OUR AGENTIC AUTOMATION PROCESS

From manual workflow to agentic workflow automation in production.

We start with one workflow your team already runs by hand, map every step and exception, and build agents that handle it end to end. Each workflow runs in shadow mode first, then goes live with approval steps, run logs and alerts you can see.

  1. 01. Map the workflow

    We sit with the people who do the work today and write down every step, system, decision and exception.

    We look at:

    • Order exceptions
    • Returns and refunds
    • Supplier POs
    • Stock transfers
    • Support triage
    • Catalogue updates

  2. 02. Design the agent system

    We decide which steps stay as fixed rules, which need an agent, how agents hand work to each other, and where a person approves.

    We design:

    • Agent roles
    • Multi-agent orchestration
    • Tool permissions
    • Approval gates
    • Fallback rules
    • Escalation paths

  3. 03. Connect your tools

    Agents act through APIs and MCP servers with least-privilege access, never through a shared admin login.

    Possible integrations include:

    • Shopify Admin API
    • Shopify Flow
    • NetSuite or your ERP
    • Gorgias
    • Klaviyo
    • Slack
    • Google Sheets

  4. 04. Test in shadow mode

    The agent works on real cases but only suggests actions. Your team compares its choices with their own.

    We evaluate:

    • Decision accuracy
    • Edge cases
    • Cost per run
    • Run time
    • Escalation rate

  5. 05. Go live and improve

    We switch on actions one step at a time, watch every run and tune prompts, rules and tools as volumes grow.

    We monitor:

    • Run logs
    • Error alerts
    • Approval queue
    • Model costs
    • Monthly review

BUILT FOR REAL OPERATIONS

Agents your operations team can trust and audit.

Every agent we ship has a narrow job, limited permissions and a record of what it did and why. If a step fails, it stops and tells a person instead of guessing. You can pause any workflow with one switch, at any time.

Model-agnosticWorks with your current stack

  • Least-privilege API access for each agent
  • Approval gates for refunds, credits and price changes
  • Full run log: inputs, tools used, outcome
  • Shadow mode before any live action
  • Hand-off to a person when confidence is low
  • One switch to pause any workflow

HUMAN-IN-THE-LOOP AUTOMATION

Where agents act alone, and where a person decides.

Not every step should be automated. We agree the limits with you before the build, write them into each agent's rules and permissions, and review them after launch. This is where we usually draw the line for commerce teams.

  1. 01.. Money leaving the business

    Refunds, store credit, supplier payments and discounts above a limit you set always wait for a person.

  2. 02.. Promises to customers

    Delivery dates, warranty decisions and complaint replies are drafted by the agent and sent after review.

  3. 03.. Catalogue and price changes

    Agents prepare bulk updates in Shopify, and a merchandiser approves them before anything goes live.

  4. 04.. Missing or conflicting data

    When the data does not add up, the agent flags the case with its reasoning instead of acting.

  5. 05.. Cases it has never seen

    A new type of case goes to your team, and we decide together whether to automate it next.

Our approach

Autonomy is earned. Each workflow starts supervised and only gets more freedom once its run logs show it makes the right call.

  1. Map the work · Set the limits · Run in shadow · Approve actions · Widen scope

AGENTIC AI AUTOMATION FAQS

Questions? We've got answers.

Still unsure? Write to us at and an AI specialist will reply.

Zapier, Shopify Flow and RPA bots follow fixed rules: if this happens, do that. Agentic AI automation gives an AI agent a goal and a set of tools, so it can read messy inputs, decide the next step and handle exceptions. Rules still suit simple, repeatable steps; agents suit work that needs judgement.

Good fits are frequent, multi-step tasks that touch several systems and need some judgement: order exceptions, returns, supplier purchase orders, stock transfers, support triage and catalogue updates. Poor fits are rare one-off tasks, or high-risk decisions with no clear rules. We score each candidate workflow before we build anything.

MCP, the Model Context Protocol, is an open standard that lets AI agents connect to tools and data through one common interface. Instead of custom code for every model, we expose Shopify, your ERP or your helpdesk as MCP servers with set permissions, so agents can use them safely and you can switch models later.

Every workflow has written limits. Agents get only the permissions their job needs, risky actions such as refunds or price changes wait in an approval queue, and each run is logged with inputs, tools used and outcome. New workflows start in shadow mode, suggesting actions before taking any.

Our AI agent development service builds a single agent, such as a shopping assistant or a support agent. Agentic AI automation joins agents, fixed rules and people into a complete back-office workflow, often with several agents handing work to each other. Many clients start with one agent and grow into automation.

An agent workflow typically takes 4 to 8 weeks from mapping to live, including a shadow-mode test period. BRISTM projects start from $5,000. Running costs depend on model usage and volume, so we measure cost per run during testing and give you a monthly estimate before launch.

We are model-agnostic. Depending on the task, we build with models from Anthropic (Claude), OpenAI (GPT) or Google (Gemini), and orchestrate them with frameworks such as LangGraph or the OpenAI Agents SDK, or n8n for simpler flows. We choose on accuracy, cost, data location and the tools you already use.

AUTOMATION INSIGHTS

Notes on automating commerce operations.

Practical guides for operations and ecommerce leads who want to know where agents help, where rules are enough and how to keep control.

Read the blog

LET'S BUILD YOUR AI AGENT

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Tell us which workflow takes too much of your team's time. As an AI agent development company, we'll show you what can be automated, what should stay human, and how we'd build it.

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