AI agents
AI agent development and workflow automation
We build AI agents and automated workflows that do real work in your systems: triaging tickets, researching leads, reconciling records, enriching catalogs. They act through defined tools with scoped permissions, ask for approval before anything risky, and log every step so you can see what they did and why.
When an agent or AI workflow makes sense
A process needs judgment on messy inputs, such as emails, tickets or documents, before a predictable next step.
People switch between several tools to gather information for one decision.
Your automation breaks whenever the input doesn't match the template it was built for.
A queue of repetitive research or triage work grows faster than the team.
You want AI to act in your systems but need control over what it can touch.
Workflow, AI step or agent?
| Approach | Choose it when |
|---|---|
| Approach01A deterministic workflow | Choose it whenThe steps and rules are known and inputs are structured. It's cheaper and easier to test. |
| Approach02A workflow with an AI step | Choose it whenMost steps are fixed, but one needs language understanding: classify, extract or draft. |
| Approach03An agent with tools | Choose it whenThe path depends on what it finds, and it needs to choose between tools to finish the job. |
| Approach04Several coordinated agents | Choose it whenRarely needed; only when one agent's context or tools can't cover a large task. |
AI agent project
A scoped build that takes one business process from manual work to a monitored agent or AI workflow.
What we do
- Map the process, its inputs, decisions and systems
- Decide where a workflow, an AI step or an agent fits
- Build the tools the agent may use, each with scoped permissions
- Connect your systems through APIs, webhooks or MCP servers
- Add approvals, limits, audit logs and evaluations
What you get
- The agent or workflow running on your infrastructure or accounts
- An evaluation suite and a review screen for its decisions
- Runbooks: how to pause it, change its limits and read its logs
Scoped after a call; the estimate depends on the process, systems and volume.
Control, not autopilot
Agents only earn trust when you can see and limit what they do.
- 01
Each tool has the minimum permissions it needs, and write actions can require human approval
- 02
Step limits, spending limits and timeouts stop runaway loops
- 03
Every step, tool call and decision is logged for audit and review
- 04
Untrusted content is kept separate from instructions to reduce prompt injection
- 05
Evaluations and monitoring catch quality drops before users do
How it works
- 01
Map
The process, inputs, decisions, systems and risks.
- 02
Design
Workflow vs agent, tools, permissions and approvals.
- 03
Build and evaluate
The agent, its tools and an evaluation set of real cases.
- 04
Run and review
A monitored rollout with a human in the loop, then wider use.
AI agent questions
Software in which a language model decides which steps to take and which tools to use to finish a task, instead of following a fixed script. In business use, agents act through defined tools with limits and approvals.
No. When the steps and rules are known, a deterministic workflow is cheaper, faster and easier to test. We use agents only where the path depends on what the task uncovers.
Through tools we define: API calls, database queries or Model Context Protocol (MCP) servers, each with scoped permissions. Actions that change data can require human approval.
Least-privilege tools, approval gates for risky actions, step and spending limits, logging of every decision, and evaluations before and after launch.
High-volume, well-understood work where mistakes are cheap to catch, such as ticket triage, lead research, document extraction or catalog enrichment, with a person reviewing the output at first.
Scope an AI agent
Tell us which process you want to automate, which systems it touches and how it's done today. We'll reply with next steps.
Prefer email? Write to webguru.com.pk@gmail.com