Informational guide to AI agents
What is an AI agent?
An AI agent is a software system that receives a goal, interprets its situation, selects the next step and uses tools to take action within defined boundaries. Agentic AI is the broader term for these goal-directed systems. Unlike a standard chatbot, an agent can work through multiple steps and affect other systems. It therefore needs explicit permissions, control points and logging.
Last updated 31 August 2026 · Patrick Petcu
AI agent, chatbot, assistant, automation or model?
The terms are often treated as synonyms, but they describe different levels of capability and responsibility.
AI agent
Works toward a goal, selects the next step, uses tools and can repeat an observe, plan, act loop until the task is complete or must be handed over.
Chatbot
Primarily holds a conversation. It may answer well without independently planning or taking action in other systems.
AI assistant
Helps a person write, search or analyse. A person normally initiates and controls each important action.
Automation
Mostly follows predefined rules and steps. An agent can choose dynamically between steps, which also creates more failure paths.
Generative model
Produces or assesses text, images, code or other data. The model is a component, not the complete agent system.
Seven components of an operational agent system
Goals and instructions
Define the task, quality requirements, stop conditions and what the agent must not do.
Tools and integrations
Provide controlled access to systems such as CRM, email, documents, search or internal APIs.
Context and state
Keep relevant evidence, earlier steps and current status available without exposing unnecessary data.
Reasoning and planning
Select a plausible sequence of steps. The plan is a working proposal and needs controls when consequences matter.
Action loop
Observe the result of a step, decide what remains and continue, stop or escalate.
Permissions and guardrails
Limit data, tools, amounts, recipients and actions. Risky steps require human approval.
Logs and evaluation
Record input, tool calls, outcomes, failures and approvals so quality can be tested and incidents investigated.
Practical examples for Swedish companies
These examples illustrate possible work patterns, not promised outcomes or claims about existing client deployments.
Inbound lead response
Summarise a form, check required fields, suggest a category and draft a response for a salesperson to approve.
Customer support triage
Classify a case, retrieve relevant instructions and propose a response or escalation. A person takes over when evidence is uncertain or the case is sensitive.
Administration and documents
Read approved documents, extract defined fields and place a proposal in the right queue without approving payments or contracts.
Internal knowledge
Search selected internal sources, compile an answer with references and flag missing or conflicting evidence.
Follow-up
Identify overdue next steps, prepare a reminder and let the owner verify recipients and content before sending.
When should you not use an agent?
Do not choose an agent merely because a task involves AI. A simpler solution is often cheaper, clearer and easier to test.
Fixed rule or integration
Use standard automation when inputs, decisions and sequence are stable.
Search or template
Use search, form validation or a template when the task does not require dynamic planning.
Human-led assistant
Use a suggestion system when judgement requires accountability, empathy, negotiation or consequence analysis.
No automation
Keep the task with a person when it is rare, hard to measure or carries high legal, financial or security risk.
A bounded one to two week pilot
The aim is to learn whether one small task can be handled accurately and controllably, not to automate an entire function.
1. One task
Select a recurring step with clear input, output, owner and stop conditions.
2. Isolated access
Allow only necessary test systems or bounded data. Prefer read-only or reversible actions first.
3. Human approval
Require review before external messages, data changes, orders or other consequential actions.
4. Metrics
Set a baseline and measure accuracy, failure types, manual effort, lead time and escalation rate.
5. Evaluation
Run representative cases for one to two weeks. Then stop, adjust or proceed at limited scale.
Risks and controls
Data access
Minimise data, separate environments, log access and apply the same confidentiality and retention requirements as other systems.
Prompt injection and untrusted input
Treat websites, documents and messages as untrusted. Do not let their content alter agent permissions or governing instructions.
Excessive permissions
Apply least privilege, tool-specific allowlists and separate approval for irreversible actions.
Hallucination and other errors
Require source references where possible, validate structured output and escalate when evidence or confidence is weak.
Missing monitoring
Log events, configure alerts and keep running recurring test cases after a successful pilot.
No rollback or owner
Name an accountable owner and define a stop control, rollback and a process for correcting data or communication.
From definition to decision and implementation
AI Lead Engine in 48 hours
A bounded implementation to classify, route and follow up inbound leads.
AI for business 2026
The broader guide to selecting, building and operating AI initiatives.
AI tools for business
Compare tools by workflow, data, integration and control.
AI consultant
The commercial route for implementation and prioritisation support.
Case studies
See published examples and experience without mixing them into the definition.
Blog
Further reading on AI, automation and digital systems.
Sources and further reading
The controls above summarise established guidance. Consult the original sources when designing governance and security.
Frequently asked questions about AI agents
Is ChatGPT an AI agent?
A generative chat service is not automatically an agent. It becomes part of an agent system when it receives goals, tools, state and authority to choose and execute multiple steps within defined boundaries.
Must an AI agent be fully autonomous?
No. An agent can propose, prepare or execute selected steps while still requiring human approval for important decisions. Autonomy is a design variable, not a requirement.
How can a company start safely?
Choose one task, isolate access, add human approval, define metrics and evaluate representative cases for one to two weeks.
When is standard automation better?
Standard automation is better when rules and sequence are stable. It is usually easier to predict, test and operate than an agent that can choose among several possible steps.
Need to determine whether a workflow requires an agent?
An AI audit can scope the task, data, risks, control points and metrics before you invest in a pilot.
Patrick works with practical AI and automation implementation. Larger multidisciplinary engagements can be coordinated through Haien. Haien