AI agents
Not every task calls for an AI agent.
AI agents reason, plan, and act across multistep workflows, on live systems. That is what makes them valuable, and it is also what brings new risks in cost, latency, and trust. So before an agent goes into production, we decide with you whether the task really belongs to it: sometimes the right answer is a person, sometimes a simple automation.
This page adapts the Gartner guide “Is Agentic AI Right for Your Use Case?” (2026). The diagrams and how we apply it to AInda's method are our own.
Related Gartner research: When to Use or Not to Use AI Agents (June 2025)
The difference
Generative AI and agentic AI are not the same thing.
They are often treated as interchangeable. Agentic AI builds on generative AI and adds autonomy, and that changes the right use, the cost, and the risk.
Generative AI
Creates content (text, images, code) in response to a prompt. It excels at synthesis, creation, and summarization.
- Responds to one prompt at a time
- Needs a person to act on the result
- Works from what it learned, not from live systems
- More predictable outputs and lower operational risk
Agentic AI
Reasons, plans, and acts across multistep workflows, using tools, memory, and external systems, with minimal human input.
- Chains multiple reasoning steps
- Acts on its own, using tools
- Works on live production systems
- Brings new risks in cost, latency, and trust
That is why, at AInda, every agent goes into production with a human owner, defined authority, measured cost per task, and a record of what it did.
Where each task goes
Two questions decide who does the work.
Gartner proposes a simple way to route any task to the right way of getting it done. We apply it in the Diagnostic, process by process.
Does the task require human decision-making or judgment?
Human expert
For example: AI and software engineers, solution architects, platform engineers.
Does it require semantic reasoning, resolving ambiguity, or multistep planning?
LLM-based AI agent
On the platforms you already have: CRM, ERP, productivity, and workflow platforms.
Deterministic automation
RPA, business process orchestration, rules-based automation, API integrations, scripts.
When the answer is a person
The agent can prepare the case, but the decision stays with whoever holds the authority. In our design, that is a human gate: blocking, before the action, and recorded.
When the answer is an agent
It runs, whenever possible, on the platforms you already have, with an authority matrix, a behavior SLA, and measured cost per task.
When the answer is automation
A fixed rule does the job for less, with less risk. We say so in the Diagnostic, even when the recommendation is not to use an agent.
Maturity
Where agentic AI stands today.
In its 2026 Hype Cycle for Agentic AI, Gartner separates what you can adopt now from what is still maturing. Here is how we read each band in our operations.
Ready to invest in now
AI coding agents, agentic AI, Model Context Protocol (MCP)
This is the foundation of what we deliver. MCP is one of the open formats that hold what we build for you.
Maturing
Multiagent systems, agentic analytics, AI governance
AI agent teams already run within well-bounded scopes, and that is how we put them into production: with authority limits, human gates, and telemetry from day one. Governance is the foundation, and that is the role of AInda Controle (our control layer).
Emerging
World models, human-agent collaboration workspaces
We watch closely, without betting your operations on it.
Bands: Gartner, Hype Cycle for Agentic AI (2026). How we read each band is AInda's.
From use case to pilot
Choosing how to implement is part of the method too.
Once you know whether the task belongs to an agent, Gartner proposes a structured path from use case analysis to pilot. It fits the first step of our offering, Understand, and the Agentic Sprint Zero.
0Prework
Map the implementation categories available.
At AIndaAn inventory of what you already have: platforms, automations, and agents in use.
1Use case analysis
Pinpoint the AI capabilities the use case needs, aligned with its functional requirements.
At AIndaAI and Agent Diagnostic and Executive Agentic Thesis.
2Technical requirements
Turn the use case into technical requirements that guide vendor selection.
At AIndaFunctional and evidence specifications, approved at the Specify gate.
3Candidate categories
Shortlist the categories that fit the project scope, the team, and the risk tolerance.
At AIndaAuthority and Delegation Matrix: what the agent may do, up to what limit, and who answers for it.
4Implementation method
Pick how to implement.
At AIndaAn execution plan with acceptance criteria, approved at the Plan gate, under a multi-harness policy.
5Pilot and refinement
Pilot, gather feedback, and refine the use case; if needed, start over.
At AIndaAgentic Sprint Zero: narrow scope, real volume, and telemetry from day one.
Steps adapted from Gartner, “Is Agentic AI Right for Your Use Case?” (2026). The “At AInda” column is our own.
Which of your processes call for an agent?
It is the first question of the AI and Agent Diagnostic, and the answer may be “none, yet.”