Investigation and research
Gathering and synthesising information from many sources into structured findings.
Technology / Generative and Language
Agentic AI
Agentic AI moves AI from answering to doing. Agents can break a goal into steps, use tools, check results and adjust. That capability is powerful and needs clear boundaries on what an agent may see, change and decide.
What is Agentic AI?
Short answer
Agentic AI describes AI systems that act with a degree of autonomy to achieve goals: they plan sequences of steps, select and call tools such as search, databases, APIs or applications, observe the results, and revise their approach, usually using large language models for reasoning within limits set by their designers.
An AI agent typically combines a language model for reasoning, a set of tools it is allowed to use, memory of the task so far and a loop that continues until the goal is met or a stopping condition is reached. More complex designs use several agents with different roles, such as a planner, researchers and a reviewer.
The key design question is authority. Reading information is low risk; sending emails, changing records or spending money is not. Well-governed agentic systems define permissions per tool, require human approval for consequential actions, limit steps and costs, log every action and are tested against manipulation and failure.
Related concepts
- LLMs
- Tool calling
- Planning
- Multi-agent systems
- MCP
- Workflow automation
- Guardrails
How it works
How does Agentic AI work?
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Receive a goal
A user or system gives the agent an objective, with context and constraints.
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Plan
The model reasons about steps needed and selects the next action.
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Act through tools
The agent calls permitted tools such as search, databases, APIs or code execution.
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Observe and adjust
Results are evaluated and the plan is updated, looping until done or stopped.
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Deliver or escalate
The agent returns results, proposes actions for approval or hands over to a person.
Applications
Where is Agentic AI useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
IT and operations
Diagnosing incidents by checking logs, metrics and recent changes.
Customer resolution
Completing multi-step service requests across several systems within limits.
Software engineering
Writing, testing and fixing code in sandboxed environments with review.
Back-office processing
Reconciling data and preparing transactions for human approval.
Data analysis
Querying data, running analyses and explaining results.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Agents can pursue wrong paths confidently, wasting time and cost.
- Malicious content can manipulate agents through prompt injection.
- Long, multi-step tasks compound small errors.
- Behaviour is harder to test than fixed workflows.
- Agents are unnecessary where steps are known and stable; workflows are cheaper and safer.
Common misconceptions
Agents can safely be given broad system access.
Broad access multiplies risk. Narrow, permissioned tools are essential.
More agents always perform better.
Multi-agent systems add coordination overhead and failure points; a single agent is often enough.
Autonomous means unsupervised.
Well-designed agents operate within limits, with monitoring and human approval for important actions.
Comparison
Agents compared with workflows and chatbots
Choosing the right pattern keeps solutions as simple and safe as possible.
| Aspect | Chatbot | AI workflow | AI agent |
|---|---|---|---|
| Main role | Answers questions | Executes predefined steps with AI inside | Plans and executes steps towards a goal |
| Who decides the steps | Not applicable | The designer | The agent, within limits |
| Best for | Information and guidance | Stable, repeatable processes | Variable, multi-step tasks |
| Predictability | Moderate | High | Lower, needs stronger controls |
| Typical controls | Content guardrails | Validation and review steps | Permissions, approvals, budgets, tracing |
Decision guide
How to evaluate Agentic AI for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| Do the steps genuinely vary from case to case? | If not, a workflow is simpler, cheaper and easier to audit. |
| Which actions could cause harm if wrong? | These need approval gates or should be excluded. |
| What tools and data does the agent need, at minimum? | Least privilege reduces exposure to errors and attacks. |
| How will we trace and review agent behaviour? | Without tracing, errors cannot be diagnosed or audited. |
| How will we test against manipulation? | Prompt injection and adversarial inputs are real threats to agents. |
AARHIT services
Services that apply Agentic AI
How we put this technology to work for organisations.
Automation
Agentic AI and Autonomous Agents
Design AI agents that plan and act through approved tools within defined permissions, budgets and approval gates.
Explore service AutomationAI Workflow Automation
Embed language models, classifiers and retrieval into business workflows with validation and review steps.
Explore service Technology & EngineeringAI Security, Governance and Ethics
Put policies, risk assessments, security controls and accountability around AI systems throughout their life.
Explore service Technology & EngineeringAI API and Systems Integration
Connect models and AI services with ERP, CRM, databases and legacy systems through secure, observable interfaces.
Explore serviceRelated technologies
Industries
FAQ
Agentic AI FAQs
What is an AI agent?
An AI agent is a system, usually built on a large language model, that works towards a goal by planning steps, using tools such as search or business applications, observing results and adjusting its approach.
What is the difference between agentic AI and generative AI?
Generative AI creates content in response to prompts. Agentic AI uses generative models as a reasoning engine to take actions through tools and complete multi-step tasks.
What are multi-agent systems?
Systems in which several AI agents with different roles collaborate or hand off work, such as a planner directing research and writing agents, often with a reviewer agent checking results.
What is prompt injection in agentic AI?
An attack where instructions hidden in content an agent reads, such as a web page or email, attempt to make the agent ignore its instructions or misuse its tools.
How should organisations govern AI agents?
By mapping authority per action, using least-privilege tools, requiring approvals for consequential actions, setting limits on steps and spend, logging all actions, testing adversarially and assigning an accountable owner.
Next step
Exploring Agentic AI?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.