Tasks too variable for fixed automation
Each case needs different lookups and actions, so rule-based workflows become huge and brittle.
02 / Automation
Agentic AI and Autonomous Agents
AI agents can plan multi-step work, call tools, check their results and try again. That makes them useful for tasks too variable for fixed workflows. It also means their authority has to be designed carefully. We build agents that do real work inside clearly drawn boundaries.
At a glance
- Service
- Agentic AI and Autonomous Agents
- Pillar
- Automation
- Typical starting point
- Agent use case and authority mapping
- Typical first phase
- 6 to 10 weeks for a governed pilot agent
- Key technologies
- Agentic AI, Large Language Models, Generative AI
Durations depend on scope, data readiness and integration needs.
What is Agentic AI?
Short answer
Agentic AI refers to AI systems, usually built on large language models, that pursue a goal by planning steps, choosing and calling tools such as search, databases or business applications, observing the results and adjusting their plan, with limits on what they are permitted to see, change and spend.
A workflow follows steps someone designed in advance. An agent decides some of those steps itself. That flexibility is valuable when tasks vary from case to case: investigating an incident, preparing a research brief, reconciling a complex account or resolving a multi-part customer request.
The engineering challenge is control. An agent with access to email, payments or production systems can cause real harm if it misreads a situation or is manipulated by malicious input. So we begin with an authority map: what the agent may read, what it may propose, what it may execute on its own and what always needs human approval.
We then build the agent with narrow tools, clear permissions, spending and step limits, full tracing of its reasoning and actions, and tested recovery when things go wrong.
Business problems
What problems does Agentic AI solve?
These are the situations where organisations most often find this service worthwhile.
Experts spending time on investigation
Senior staff gather information from many systems before they can make a decision.
Agent pilots with unclear risk
Teams have built impressive agent demos but cannot explain what the agent is allowed to do in production.
Fragmented multi-system tasks
Resolving one request means working across tickets, CRM, knowledge bases and internal tools.
Overnight and after-hours workload
Routine investigations and preparation pile up outside working hours.
Lack of auditability
Nobody can reconstruct why an automated system took a particular action.
Use cases
Where can Agentic AI be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| IT operations | Incident investigation agents that gather logs, check recent changes and propose remediation for approval. |
| Customer service | Agents that resolve multi-step requests across order, billing and shipping systems within set limits. |
| Research and analysis | Agents that search approved sources, compile evidence and produce structured briefs with citations. |
| Finance operations | Reconciliation agents that investigate mismatches and prepare adjustments for accountant approval. |
| Software engineering | Coding and testing agents working in sandboxed repositories with pull request review. |
| Procurement | Agents that compare supplier quotes against policy and prepare recommendations for buyers. |
Who should consider it
- Organisations with complex, variable tasks that span several systems
- Technology leaders who need agent pilots with defined governance
- Operations teams with investigation-heavy work
- Product teams building agent capabilities into software
When it may not be the right fit
If the steps are the same every time, a conventional workflow is cheaper, faster and easier to audit than an agent. We will recommend the simpler option where it fits.
How AARHIT approaches it
How we deliver Agentic AI
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Use case and authority mapping
We define the goal, the tasks involved and, for each possible action, whether the agent may read, propose, execute or must seek approval.
Output: an authority map and risk assessment agreed with the business owner.
Stage 2 of 6
Tool and data design
Each capability becomes a narrow tool with typed inputs, permission checks, rate limits and logging, rather than broad system access.
Output: tool specifications and data access rules.
Stage 3 of 6
Agent architecture
We choose the pattern: single agent, planner with executors, or multiple specialised agents, and define memory, context and stopping conditions.
Output: architecture design with limits on steps, time and cost.
Stage 4 of 6
Build with tracing
The agent is built with full tracing of prompts, tool calls, observations and decisions, so every run can be inspected.
Output: a working agent in a sandbox environment.
Stage 5 of 6
Behaviour and safety evaluation
We test task success, tool use accuracy, refusal of out-of-scope requests, resistance to prompt injection and recovery from tool failures.
Output: an evaluation report with pass criteria for release.
Stage 6 of 6
Governed rollout
The agent starts in propose-only mode, then gains execution rights for low-risk actions as evidence accumulates, with approval gates retained for consequential ones.
Output: staged rollout plan, monitoring dashboards and an incident procedure.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Models | Frontier LLMs with tool calling, plus smaller models for routing and checks |
| Agent frameworks | LangGraph, OpenAI Agents SDK, Semantic Kernel, AutoGen or custom orchestration |
| Tool protocols | Function calling and Model Context Protocol (MCP) servers with permission layers |
| Memory and retrieval | Vector stores, knowledge graphs and session state stores |
| Guardrails | Policy checks, input and output filters, sandboxed execution environments |
| Observability | Run tracing, cost tracking and evaluation tooling |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Faster resolution of complex, multi-step cases
- Experts starting from a prepared investigation rather than a blank screen
- Consistent handling with a full record of every action taken
- A clear, auditable boundary between agent actions and human decisions
- A reusable tool and governance layer for future agents
How long does implementation generally take?
Typical timeline
A governed pilot agent for one defined use case typically takes 6 to 10 weeks, including authority mapping and safety evaluation. Expanding its permissions or adding agents is planned in further increments once operating evidence is available.
What determines complexity?
- Number and sensitivity of tools the agent needs
- Consequences of a wrong action
- Length and variability of the tasks
- Quality of the knowledge sources available
- Regulatory expectations around automated decisions
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Start in propose-only mode
Letting an agent recommend actions for human approval builds evidence about its behaviour before it is trusted to act.
Treat every input as potentially hostile
Emails, web pages and documents can contain instructions intended to manipulate agents. Tools and permissions must assume this.
Limit steps, time and spend
Agents can loop or pursue unproductive paths. Hard limits prevent runaway cost and give predictable behaviour.
Keep a human accountable
Every agent needs a named owner responsible for its behaviour, permissions and incident response.
| Risk to consider | How we manage it |
|---|---|
| Unauthorised or harmful actions | Authority mapping, least-privilege tools, approval gates and allow lists for consequential operations. |
| Prompt injection | Separation of instructions from content, input screening, restricted tool scopes and adversarial testing. |
| Runaway cost or loops | Step, time and token budgets with automatic termination and alerts. |
| Opaque decisions | Full tracing of reasoning, tool calls and results, retained for audit. |
| Over-reliance by users | Clear interface cues about agent confidence and a simple way to reject or correct outcomes. |
FAQ
Questions about Agentic AI
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent works towards a goal by planning steps and using tools, such as querying a database or updating a ticket, and can complete tasks rather than only respond.
Are autonomous agents safe to use in business?
They can be, when authority is designed deliberately. That means narrow tools, least-privilege access, approval for consequential actions, limits on steps and spend, full logging and tested failure handling.
What is a multi-agent system?
A design where several specialised agents collaborate, for example a planner that assigns work to research, analysis and writing agents. It can help with complex tasks but adds coordination cost, so we use it only when a single agent is not enough.
What is Model Context Protocol (MCP)?
MCP is an open protocol for connecting AI models to tools and data sources in a standard way. It simplifies integration, but permission checks and logging still need to be designed around each tool.
How do you test an AI agent?
We test task completion on realistic scenarios, correctness of tool use, behaviour on out-of-scope or malicious inputs, adherence to limits and recovery from tool errors. Tests are rerun whenever the model, prompts or tools change.
How long before an agent can act without approval?
There is no fixed period. Low-risk actions may be automated after a few weeks of consistent propose-mode results. High-impact actions such as payments or customer commitments usually keep a human approval step.
Next step
Considering Agentic AI for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.