What exactly counts as an "agent," versus just a chatbot?

A chatbot answers a question and stops there. An agent gets a goal and works toward it. It reasons through steps and uses tools, then takes action with limited supervision. In practice, that means an agent can research, draft, update a record, or trigger a task on its own, where a chatbot only responds. This training makes that line clear before it teaches anything else.

Do we need developers on the team for this?

No. The whole program runs on no-code and low-code platforms, built for business and operations people: marketing, operations, innovation, support, product. The people who own the workflow are the ones who end up building the agent.

Does anyone need to already know how to code?

No. If your team can use everyday business software and describe their own processes clearly, they can build an agent here. Coding and data-science backgrounds aren't part of the bar. A separate technical track can be scoped on request for anything developer-deep, like custom frameworks.

How long does it take, and where does it run?

Two full days, about 13 hours, delivered onsite, live online, or in half-day blocks so daily work keeps moving, with the build modules landing best when participants work on their own live processes.

Which platforms does the build session use?

Mainstream no-code tools carry the core skills: Microsoft Copilot Studio, custom GPTs, and current agent builders, with an overview of OpenAI Agent Builder and other vendor platforms. Deterministic automation tools like n8n and Make come up only at the level of knowing when they fit better than an agent. Building those pipelines is what our AI automation training covers instead.

Will we build something on our own workflow, or just watch a demo?

In the build modules, your team scopes a live process, builds a working agent on it, and publishes it before the session ends. We run a short discovery call beforehand so the platforms and depth match your systems, and what you leave with is ready to use.

How do governance and KVKK change once an agent is live?

A full module covers it. Because an agent acts on its own, data access, permissions, monitoring, and audit trails matter more than they would for a simple chatbot. We work through what KVKK and GDPR expect from autonomous systems, and help you set guardrails, human checkpoints, and a governance policy your team will follow in practice.

What decides the number?

Group size, delivery format, customization depth, and location decide it, so no two quotes look alike. Share your teams and the workflows you have in mind, and we'll price a proposal for that scope specifically.

AI Training · AI Agents

AI Agents Training for Business Teams

A chatbot answers a question and stops. An agent works differently — it reasons toward a goal, picks up tools along the way, and takes multi-step action on its own, with little hand-holding.

This training teaches business and operations teams to build that kind of agent on no-code platforms, then govern it responsibly once it's live. Everything gets built on the workflows you already run, not a generic demo.

modules: 6
hours: 13

Some of the 500+ brands we've worked with

Selected brands we’ve worked with across industries.

See all references

Most people assume an agent is just a chatbot with extra steps. That's the common mistake: a chatbot waits for a question. Give an agent a goal and it reasons through the steps. It calls tools and takes action on its own.

Boards named agents the 2026 priority because an agent that acts on its own reaches further into your systems than a chatbot ever could — worth setting up carefully. This program skips hand-built Python frameworks: teams decide which processes need an agent rather than a fixed automation workflow, build one on a no-code platform, and govern data access and monitoring under KVKK and GDPR. The AI training catalog covers the rest.

A chatbot answers, an agent acts

A chatbot replies; an agent reasons through a goal, uses tools, and takes multi-step action — widening what your team can delegate, and the care the setup needs.

The board named agents the 2026 priority

Teams rush to build agents where a simple workflow would do; the harder skill is knowing which processes genuinely need autonomy.

Why did last year's ChatGPT training fade?

Many organizations trained on ChatGPT in 2025 and saw usage fade; agents stick when teams can build and govern their own workflows.

No-code puts agent-building in business hands

Copilot Studio, custom GPTs, and agent builders let business teams ship an agent without code, making design and judgment essential.

Guard the data before you grant the autonomy

Agents can reach data and systems chatbots never touch; guardrails, human checkpoints, and KVKK-aware governance come before launch.

01
From chatbots to agents: what agentic AI really is
90 minBeginner
A hype-free grounding before anyone builds anything: what separates a chatbot from a workflow, then what makes a true agent. The module also explains why an agent's autonomy is both its value and its risk.
What separates a chatbot from a workflow, then from a true agent
The anatomy of an agent: model, tools, memory, and planning
When a simple workflow beats an agent, and when it doesn't
Where autonomous agents already show up in tools you use
Realistic first use cases
02
Choosing your agent-building stack
90 minBeginner
The no-code and low-code options, and how to pick between them: where each platform is strong, what it actually costs to run agents day to day, and how to match a stack to your team's skills and data.
No-code and low-code platforms: Copilot Studio, custom GPTs, and agent builders
OpenAI Agent Builder and vendor agent platforms at a glance
Where deterministic automation tools like n8n and Make fit, and where they don't
Build-versus-buy and the real cost of running agents
Matching a platform to your team's skills and data
03
Building your first business agent, hands-on and no-code
180 minIntermediate
The core build session starts with one live process. Your team gives the agent the knowledge and tools it needs, then sets its boundaries. A working agent goes live in the room.
Scoping one live process from your own work
Knowledge sources and connected tools, given to the agent
Instructions, boundaries, guardrails
Testing an agent against edge cases before you trust it
A working agent, published live in the session
A reusable design brief for the next agent
04
Agents for your department's workflows
150 minIntermediate
The highest-value agent patterns per function, worked on your own material: research and reporting, content QA, support triage, and lead qualification. You leave with a playbook built around your own functions.
Research and reporting agents for marketing and SEO
Content review and quality-assurance agents
Support triage and customer-response agents
Lead qualification and routing agents
A per-function playbook
05
Multi-step and multi-agent workflows
120 minIntermediate
For when a single agent runs out of runway. This module covers orchestrating several steps and several agents without losing control, with human checkpoints and safe fallbacks built in from the start.
Orchestrating several steps without losing control
Human-in-the-loop checkpoints and approvals
Escalation paths and error handling, with safe fallbacks
Handoffs between agents, and back to a person
Keeping a multi-agent system observable
06
Governance and secure rollout
150 minAdvanced
What it takes to trust an agent once it's live. Data access, KVKK and GDPR obligations for systems that act on their own, ongoing monitoring, and a pilot-to-scale plan you can actually measure.
Data access and permissions for autonomous agents
KVKK and GDPR obligations for agents that act on their own
Agent output, evaluated and monitored over time
Logging and audit trails for accountability
A pilot-to-scale plan with measurable ROI

What you will learn

The chatbot-versus-agent line, obvious now
Reject agents not worth building
Build and publish a working agent on Copilot Studio or a custom GPT
Connect an agent to your knowledge sources and tools, with guardrails set
Set human-in-the-loop checkpoints and safe fallbacks
Govern agents under KVKK and GDPR: access, logging, oversight
Track agent output before launch, then after it ships
Plan a pilot-to-rollout path anyone can measure

Who should attend

Digital and growth leads, including marketing
Operations and business-transformation managers
Innovation and AI-adoption owners
Customer support and service team leads
Product managers and analysts exploring agents
HR and L&D leads running AI-upskilling programs

Agent-building takes two days, usually split into half-day sessions. Onsite and live-online formats cover the same syllabus, and the build modules work best when participants bring their own processes to work on.

Format: Onsite or live online
Duration: 2 days (about 13 hours, can be split into half-day sessions)
Group size: Up to 20 participants per group
Language: English or Turkish
Materials: Agent design briefs, a prompt library, and an exercise workbook
Certificate: Certificate of completion

AI Automation with n8n6 modules · 13 hoursAI automation training for ops and marketing teams: build, test, and govern workflows with n8n, keep a person in the loop where it matters, and hold clear data boundaries throughout.View training

OpenClaw6 modules · 12 hoursInstructor-led OpenClaw training for business teams piloting a fast-moving, self-hosted personal AI assistant: a contained setup, scoped business workflows, review discipline before anything ships, and a governed call on whether to roll it out.View training

Hermes Agent6 modules · 13 hoursInstructor-led Hermes Agent training for business and operations teams: safe self-hosted setup and scoped business workflows under human-in-the-loop governance for a fast-moving open-source agent.View training

Salesforce Agentforce6 modules · 13 hoursInstructor-led Salesforce Agentforce training for CRM teams: agent and topic setup, grounding on Salesforce data, guardrails, and a governed rollout across service and sales workflows.View training

Which workflow do you have in mind, and who needs to be in the room? We'll pick the platforms and the depth, and build the session around that one process.

A learner fitting a shape into a practice bench beside a completed sequence

What exactly counts as an "agent," versus just a chatbot?
A chatbot answers a question and stops there. An agent gets a goal and works toward it. It reasons through steps and uses tools, then takes action with limited supervision. In practice, that means an agent can research, draft, update a record, or trigger a task on its own, where a chatbot only responds. This training makes that line clear before it teaches anything else.
Do we need developers on the team for this?
No. The whole program runs on no-code and low-code platforms, built for business and operations people: marketing, operations, innovation, support, product. The people who own the workflow are the ones who end up building the agent.
Does anyone need to already know how to code?
No. If your team can use everyday business software and describe their own processes clearly, they can build an agent here. Coding and data-science backgrounds aren't part of the bar. A separate technical track can be scoped on request for anything developer-deep, like custom frameworks.
How long does it take, and where does it run?
Two full days, about 13 hours, delivered onsite, live online, or in half-day blocks so daily work keeps moving, with the build modules landing best when participants work on their own live processes.
Which platforms does the build session use?
Mainstream no-code tools carry the core skills: Microsoft Copilot Studio, custom GPTs, and current agent builders, with an overview of OpenAI Agent Builder and other vendor platforms. Deterministic automation tools like n8n and Make come up only at the level of knowing when they fit better than an agent. Building those pipelines is what our AI automation training covers instead.
Will we build something on our own workflow, or just watch a demo?
In the build modules, your team scopes a live process, builds a working agent on it, and publishes it before the session ends. We run a short discovery call beforehand so the platforms and depth match your systems, and what you leave with is ready to use.
How do governance and KVKK change once an agent is live?
A full module covers it. Because an agent acts on its own, data access, permissions, monitoring, and audit trails matter more than they would for a simple chatbot. We work through what KVKK and GDPR expect from autonomous systems, and help you set guardrails, human checkpoints, and a governance policy your team will follow in practice.
What decides the number?
Group size, delivery format, customization depth, and location decide it, so no two quotes look alike. Share your teams and the workflows you have in mind, and we'll price a proposal for that scope specifically.

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AI Agents Training for Business Teams