Agentic AI is an autonomous artificial intelligence system that can take action to achieve a goal. This goal could be to onboard a new employee, monitor budget and send monthly reports to the finance team, automatically place orders to maintain stock, or provide real time feedback to clinicians to adjust their treatments. An agentic system is defined by its sustained pursuit of goals with the ability to adjust actions as context changes. It can break a high-level goal into smaller tasks and keep working towards it until it is reached.
Agentic AI systems usually work in a multi-agent system, where each of the several “agents” carries out a specific task to achieve the goal. In an onboarding system, for example, one agent is tasked with updating the employee records, another is responsible for scheduling and sending out invites for orientation sessions, and another agent sets up the email credentials. Each agent works individually to complete its piece, and they pass information between each other. This is not unlike a human onboarding HR team, the only difference being that each agent has a much more specific function than an HR team member. Well, at least in the agentic systems of today. This could quickly change though, as agentic AI is becoming more and more advanced and capable of carrying out more complex tasks.
How is agentic AI different from generative AI?
Agentic AI also uses the same large language models that power generative AI but the difference is in their purpose. While generative AI is concerned with creating content in response to prompts (text, images, videos), agentic AI is more concerned with completing tasks. Generative AI is reactive, meaning it works only when you prompt it, while agentic AI is proactive, meaning it can take decisions without having to be prompted. Generative AI will write an email for you if you ask it to, but an agentic AI could decide whom to email, draft and send it, then even follow up if it gets no response.
We have seen many representations of agentic AI in sci-fi and superhero movies, although we aren’t quite at that stage of evolution yet. But today’s systems like Wolken’s AI are laying the foundation.
If your mind is running with the possibilities of agentic AI, you are not alone. Already, agentic AI is being used in IT support, HR service delivery, finance automation and many other enterprise functions. While research into using agentic AI for robotics is still early, the first studies available in the public domain point to an exciting future.
How does agentic AI work?
- Setting the goal and collecting data
At this stage, the user sets the goal so the agent can analyze what needs to be done, what data is available and what is missing. Let us follow the journey of a finance agent to understand this better. The goalsetting would involve the following main components:- Objective: Close the books by the 5thworking day. Reconcile sub-ledgers to GL, resolve exceptions, deliver packs to the CFO and record all actions for audit.
- Scope: entities, currencies and accounting standards in play.
- Guardrails: segregation of duties among agents, approval limits for journal entries, month-end checklists, signatory matrices and audit trails. Anything involving sensitive accounts that require human sign-off.
- Required data sources: ERP systems, GL, invoices, pending entries, open PO/GRNI balances, bank feeds, payroll, relevant policies. Calendars to look for cut-off dates and local holidays that affect cash application.
- Understanding the context based on the data
Next, the system interprets the information given and plans its steps. It might notice that vendor payments need reconciliation, accounts receivable has a few outliers, and certain journal entries require human approval.
- The system assembles context from the available data sources.
- It checks data quality like missing invoices, out-of-date postings, duplicate vendor bills, unmapped accounts, etc. Each data set gets a confidence score so the agent knows where to look closer.
- The system builds a working set for the close: open items to clear (like unmatched purchase orders), expected accruals (like utilities, bonuses, freight, etc.), prepaid schedules to amortize (like annual insurance, rent), known recurring payments from prior months (maybe a particular vendor sends invoices late)
- Prioritisation and execution
The system decomposes the high-level goal into tasks to split it among agents. It orders them by dependency and estimates effort and risk. Examples of task break down:- AP agent: matches invoices to purchase orders, verifies and updates vendor records.
- GL agent: runs revenue recognition checks, executes fixed asset depreciation runs.
Throughout, every action is logged with who/what/when/why so audit can replay the process if needed.
- Learning and reflection
After the execution, the system runs a short post-close analysis. It updates the play books, sends consolidated reporting packs to the CFO and distributes reports automatically to the finance leaders or auditors.
Leader scan get a wealth of data in this step – which approvals took the longest? Which exceptions kept recurring? Which accounts caused delays and why? The finding scan then be fed back into the system so the agents can perform better next time.
How to train AI agents
In many ways, agentic systems are built on the same foundations as other AI models. They start with large scale training on diverse datasets, then tailored to specific needs. A common approach is to take a powerful base model and fine-tune it with domain-specific data and instructions. Ingesting an agentic system with an enterprise’s knowledge base, process documentation and policy rules can train it to understand the particular environment it will work in. On top of that, Reinforcement Learning from Human Feedback (RLHF) can be used to align the agent's behavior with what the users actually want it to do. In RFHF, the agent is ‘rewarded’ for desirable behaviour and ‘penalised’ for undesired outputs, in a loop so that the system slowly learns to produce more helpful outcomes.
Agentic systems also are embedded with monitoring and feedback loops themselves. As mentioned in step 4 of the previous section, agents log every action which can be reviewed by leaders to catch mistakes and provide corrective feedback. It is important to keep training the agents with new data like new requests, updated policies, customer feedback, etc. Periodically running the agent through hypothetical scenarios to see how it performs and fine tune it accordingly, is also helpful. Training an agentic system is not a one-and-done project but an ongoing process of reiteration and adjustment.
Path forward
There are some critical limitations of agentic AI. Data quality and readiness can determine how well agents perform, many capabilities are still maturing, and in some cases the link to clear business value is not yet fully established. Careful planning can mitigate many of these challenges and as the technology advances, it will steadily take on more complex workflows.
These are not reasons to hold back, though. With the right foundations, organizations will avoid pitfalls and can experiment responsibly. As with any revolutionary technology, there will be a shakeout period where some projects fail. But best practices will crystalize from those lessons. The path forward is to recognize the potential as well as the limitations and to choose a platform that can grow with you.
Wolken, for example, has developed a no-code platform that makes it easy for enterprises to plug it with their existing systems. Wolken AI is designed to respect guardrails, while giving teams flexibility to build and adapt workflows to suit their specific needs. So, enterprises can start small, scale safely and capture value early as agentic AI matures over time.
Agentic AI is the next frontier in automation. Just as earlier waves of AI changed how we access and interpret information, agentic AI will change how we get things done.