Is it safe to let AI take actions?

With the right design, yes. We limit permissions, require approval for important actions, log everything and test thoroughly before giving an agent more freedom.

Which frameworks do you use?

We use model providers' tool-calling APIs and frameworks where they help, keeping the architecture simple enough to maintain.

How do you measure whether an agent works?

With an evaluation set of real tasks and clear success criteria, measured before launch and monitored afterwards.

AI Agent Development & Tool Calling

08 · AI & Automation04 of 07

Agents that use your tools to complete multi-step tasks, with guardrails and approvals built in.

A chatbot answers questions. An agent completes tasks: it plans the steps, uses your tools and data, checks its work and asks for approval where it should. We build focused AI agents for real business tasks, with clear limits, audit logs and people in control.

Start a project

What's included

Of agent actions logged: 100%; Of agent actions logged
Approval on anything consequential: Human; Approval on anything consequential
Typical time from prototype to production: 3–8 wks; Typical time from prototype to production

agent.tstools.ts

$ agent run --task

Live · tests passed

AI agents & tool calling · illustration

Task automationTool callingMulti-step workflowsResearch agentsData updatesApprovalsAudit logsGuardrailsTask automationTool callingMulti-step workflowsResearch agentsData updatesApprovalsAudit logsGuardrails

Sound familiar?

The problems we hear most often before someone asks us about AI agents & tool calling.

Tasks too messy for simple automation

Some work needs judgement at each step, so rule-based automations can't handle it.

AI demos that never reach production

Impressive prototypes stall because nobody planned for errors, security or approvals.

Worried about AI acting alone

Letting AI change real systems feels risky without limits and oversight.

What's included

Use-case selection

We find tasks where an agent saves real time and the risk is manageable.

Agent design

Goals, steps, tools and the points where a person must approve.

Tool integrations

Secure access to the systems the agent needs, with the minimum permissions.

Guardrails

Limits on what the agent can do, spend or change, with checks before actions.

Audit trail

Every step and decision logged, so you can see exactly what happened.

Evaluation

Test suites of real tasks to measure accuracy before and after launch.

How we do it

01/ 04

You follow every step live in Mission Control, your project dashboard: tasks, files, approvals and a launch forecast.

1
Pick the task
A focused, valuable task with clear success criteria.
2
Prototype
A working agent tested on real examples within weeks.
3
Harden
Guardrails, approvals, logging and error handling added.
4
Deploy and monitor
Gradual rollout, monitoring and regular evaluation.

100%

Of agent actions logged

Human

Approval on anything consequential

3–8 wks

Typical time from prototype to production

Built for

Who this is for.

Operations teams with complex, repetitive work

Businesses with an AI prototype to productionise

Research-heavy teams

Companies exploring AI beyond chat

Tools we useClaudeVercel AI SDKn8nZapierMakeHubSpotSupabasePython

Helpful extras

Try it yourself, or see what's built in.

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Questions

AI agents & tool calling, answered.

Something else on your mind? Ask the Zelvorm Concierge in the corner, or talk to the team.

A chatbot holds a conversation. An agent pursues a goal across several steps, using tools such as search, databases or APIs to take actions and report back.