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AI Automation & AI Agents

Turn AI from an isolated experiment into a dependable operating layer.

Build agents that understand context, use approved tools, ask for human review, and complete work across your business systems.

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The operational drag

AI pilots lose value when they cannot safely act inside real workflows.

Disconnected tools and manual handoffs make repeatable work harder to control.

01

Prompts stay disconnected

Useful AI output still needs a person to copy, interpret, and route it.

02

Agent actions feel risky

Teams lack clear tool permissions, approval gates, and limits for autonomous work.

03

Decisions are hard to inspect

No shared run history explains what the agent saw, decided, or changed.

Before and after

Replace a fragile handoff with a visible operating system.

See how the same work changes when the process is connected, repeatable, and observable.

Before PUQManual and reactive

Prompts stay disconnected

Useful AI output still needs a person to copy, interpret, and route it.

Agent actions feel risky

Teams lack clear tool permissions, approval gates, and limits for autonomous work.

Decisions are hard to inspect

No shared run history explains what the agent saw, decided, or changed.

With PUQConnected and repeatable

Add intelligence to any step

Classify, summarize, extract, draft, and decide with the context already in the flow.

Let agents use approved tools

Connect agents to apps and APIs with explicit actions and business rules.

Keep people in control

Pause sensitive decisions for review and preserve a complete execution record.

Setup

Start with one process. Launch without a rebuild.

PUQ fits around the workflow your team already understands, so the first useful automation can stay focused.

01

Choose the starting signal

Start from the event your team already receives: a form, record change, schedule, webhook, or request.

02

Map the business rules

Add the checks, branches, ownership rules, and approval points that make the process safe to run.

03

Connect, test, and activate

Connect the tools in your stack, test the full path with real context, then publish when the team is ready.

Use the apps, APIs, webhooks, and approval tools already in your stack.

How does it work?

One visual path from signal to result.

Design the process in the same order the work happens, then let PUQ coordinate the handoffs.

  1. Step 1

    A signal starts the flow

    Receive request or message

  2. Step 2

    PUQ adds context

    Classify intent and urgency

  3. Step 3

    The right path runs

    Route or request approval

  4. Step 4

    Every run stays visible

    The team can inspect the result, handle exceptions, and improve the process over time.

Example workflows

Agentic workflows built for real operational work.

Start with bounded jobs where context, tools, and success criteria are clear.

AI triage

Classify and route inbound work

Understand intent before choosing the next path.

  1. Receive request or message
  2. Classify intent and urgency
  3. Route or request approval

Every request reaches the right owner with context.

Research agent

Research before the team acts

Collect evidence and return a structured brief.

  1. Receive research objective
  2. Search approved sources
  3. Summarize findings with links

Decision-makers receive a repeatable research package.

Action agent

Complete multi-system tasks

Use tools only after rules and permissions are satisfied.

  1. Read workflow context
  2. Select approved action
  3. Update systems and log result

AI output becomes controlled execution.

See the product in the flow

Design the agent, its tools, and its guardrails on one canvas.

Combine models, business data, deterministic steps, approvals, and monitoring in the same visual workflow.

  • Choose the model and context for each reasoning step
  • Restrict tool access and add human approval where needed
  • Inspect every decision, action, retry, and exception
Explore the workflow builder
Abstract workflow builder canvas with connected automation stepsAI Automation & AI Agents workflow

What changes for the team

A better day-to-day experience for every owner in the process.

These are the practical outcomes the workflow is designed to create across the team.

Classify, summarize, extract, draft, and decide with the context already in the flow.

Operations leadAdd intelligence to any step

Connect agents to apps and APIs with explicit actions and business rules.

Team managerLet agents use approved tools

Pause sensitive decisions for review and preserve a complete execution record.

Process ownerKeep people in control

Built for the work after launch

Keep the workflow useful after it goes live.

Add intelligence to any step

Classify, summarize, extract, draft, and decide with the context already in the flow.

Let agents use approved tools

Connect agents to apps and APIs with explicit actions and business rules.

Keep people in control

Pause sensitive decisions for review and preserve a complete execution record.

Make the next handoff automatic

Launch your first governed AI agent.

Choose one bounded job and turn it into an observable agent workflow.

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