Why AI agents are transforming business processes
Today’s businesses are seeking fast ways to streamline operations, reduce manual errors, and free employees from repetitive tasks. AI agents offer the solution: small, autonomous programs that can perceive their environment, make decisions, and act without constant human intervention. Thanks to advances in agent orchestration and the emergence of compact models like PrismML’s new Bonsai 27B, integrating AI into business workflows has never been more accessible.
Which agent is right for your workflow?
The choice of the right agent depends on three key factors:
- Model size and resources.With Bonsai 27B, you can now run a full-precision LLM on laptops and mobile devices, thanks to its 1-bit and ternary variants. This significantly reduces computational costs.
- Orchestration.Platforms like Anthropic’s (Claude) and Nokia’s new AI-RAN service provide coordination layers that connect multiple agents, ensuring consistency and traceability.
- Prompt interface.A good agent starts with a clear, structured prompt. Here’s a basic example for a travel expense approval agent:
Prompt example
You are a travel expense approval agent. You receive a request with amount, category, and justification.
1. Verify that the amount is less than €5,000.
2. Confirm that the category is among the approved ones: travel, supplies, equipment.
3. If all checks pass, respond with "APPROVED" and the reason.
4. Otherwise, respond with "REJECTED" and the reason.
Input data: {"amount": 3,200, "category": "travel", "justification": "client conference"}Implementing agentic orchestration with current platforms
As of July 2026, agentic orchestration has consolidated around a few dominant providers. Most large enterprises are adopting platforms that offer:
- Centralized agent registry
- Tracking of actions and results
- Monitoring and debugging tools
For example, Nokia’s AI-RAN platform brings these capabilities to the network edge, allowing operators to deploy agents directly into radio stations and process data in real time.
Practical example: A travel expense approval workflow
Imagine an accounting office that wants to automate the approval of reimbursement requests. The workflow can be broken down into these agents:
- Data extraction agent.Extracts amount, category, and justification from a scanned receipt.
- Validation agent.Applies business rules (spending limit, approved categories).
- Approval agent.Uses the prompt above to decide the outcome.
- Notification agent.Sends a real-time message to the accountant and employee via email or mobile app.
These agents, integrable with an orchestration engine like Anthropic’s, can be deployed in minutes on any cloud, thanks to the compact size of models like Bonsai 27B.
Best practices for successful implementation
- Keep prompts modular.Separate different logical functions (extraction, validation, decision) into independent prompts for easier maintenance.
- Document limitations always.Each agent has strengths and weaknesses; a clear log helps teams mitigate risks.
- Leverage low-bit models when possible.1-bit and ternary models like Bonsai 27B offer performance comparable to full-precision models with drastically lower power consumption, ideal for edge devices.
- Test iteratively.Use a feedback loop that includes real users to adapt agents over time.
Conclusion: Bring AI into everyday business processes
AI agents are no longer a futuristic concept; they are practical tools that can transform manual tasks, reduce errors, and accelerate time-to-value. By leveraging the latest orchestration solutions, compact models like Bonsai 27B, and careful prompt design, businesses can integrate AI into their existing workflows with minimal risk and measurable impact.
Start with a single use case, choose a suitable agent, and watch how automation improves your organization’s efficiency.