Phigz
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Agentic AI solutions

Agents that do the work, and stop when a person should decide.

An agent is useful when it can read context, call the tools you already trust, and carry a task forward. Phigz builds those workflows with explicit limits, logs and approval steps.
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The problem

A chatbot answers. An agent is asked to act: look up a record, draft a reply, file a document, or move a job to the next queue. Those are different products.

The risk is an agent with vague authority. It can reach systems it should not touch, or it completes a step that needed a person because nobody drew the line.

Phigz treats an agent as a workflow with a job description. Tools, allowed actions and the handoff back to a human are part of the design, not an afterthought.

What Phigz delivers

  • A defined job

    One workflow, the inputs it may see, the outcome it should produce, and the cases where it must pause.

  • Tool use

    Connections to the APIs, documents and internal systems the task actually needs. Unused tools stay out.

  • Control points

    Approval before a write to a customer, a payment, or a system of record. The agent prepares. A person commits.

  • An operable trail

    A record of what the agent read, which tool it called, and what a person changed before the outcome was accepted.

Where it fits

  • Customer support preparation

    The agent gathers the ticket, the account and a suggested reply. A person sends it.

  • Internal operations

    Routing requests, chasing missing information, or assembling a brief from several systems before a manager acts.

  • Document handling

    Reading a file, extracting the fields you care about, and placing a draft into the process that already reviews documents.

  • Research inside the business

    Searching approved sources and returning a sourced summary, rather than browsing the open web without a boundary.

Delivery

  1. 01

    Name the workflow

    We map the current steps, including the ones a person must keep.

  2. 02

    Choose tools and limits

    Each tool gets a purpose. Writes, sends and deletions are separated from reads.

  3. 03

    Implement the loop

    The agent plans within that boundary, calls tools, and stops at the approval you specified.

  4. 04

    Watch real tasks

    We run it on genuine examples, adjust the stops, and only then leave it with the team.

Technical capability

  • Orchestration

    The workflow is explicit code: steps, retries and stop conditions. The model does not invent new permissions at runtime.

  • System connections

    Agents connect through APIs you control. CRM, ERP and internal services are integrations, not a promise that every vendor platform is already supported.

  • Human gate

    Where an action changes a customer or a record, the interface presents the proposed action for approval.

Related product work

Published case studies are product builds. Where a page is about AI, these projects show how Phigz ships a complete product. They are not labelled as AI systems.

Related insights

Questions

Is an agent the same as a chatbot?

No. A chatbot converses. An agent is built to carry a task through tools. If you only need answers in a window, a chatbot may be enough. We will say so.

Will the agent act without anyone watching?

Only for steps we have agreed are safe to run unattended, usually reads and drafts. Actions that commit, send or pay wait for a person.

Can it connect to the software we already use?

Yes, when that software exposes an API or a stable export we can integrate. We scope the connection before building it.

Next step

Talk through this engagement.

Tell us the product, the current state, and the outcome you need. We will reply with a straight view of whether this is the right shape of work.

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