Agentic AI
development
Learning agents go beyond traditional machine learning. They enhance their decision-making by continuously processing user feedback and improving their understanding of instructions. This type of AI agent demonstrates increasingly sophisticated operation through direct performance analysis and iterative improvement mechanisms while maintaining appropriate human oversight for critical decisions.
Industry applications of agentic AI
AI agents for healthcare
- Autonomous monitoring of medical device data and health records to detect anomalies
- Clinical workflow optimisation and automated resource allocation
- Overseeing medication inventories, predicting supply needs
- Analysing patient data and treatment protocols to support clinical decision-making
- Laboratory process automation, coordinating test scheduling, monitoring equipment performance
AI agents for retail
- Inventory optimisation, monitoring stock levels, analysing sales patterns, and automatically adjusting order quantities.
- Dynamic pricing management based on market conditions, competitor pricing, and demand patterns.
- Coordinating store operations, managing staff scheduling
- Supply chain coordination, managing supplier relationships, tracking shipments, predicting delays
- Customer journey optimisation, managing loyalty programs, and coordinating omnichannel customer interactions
AI agents for entertainment
- Managing content distribution across platforms
- Analysing viewer behaviour, coordinating personalised content recommendations
- Rights management automation, monitoring content usage, tracking licensing agreements
- Production workflow coordination, managing schedules, coordinating resource allocation
- Asset management, organising media libraries, automating metadata tagging
AI agents for finance
- Risk management, monitoring transaction patterns, detecting anomalies
- Analysing market conditions and adjusting trading strategies based on real-time market data
- Portfolio management, monitoring investment performance, rebalancing portfolios
- Compliance monitoring, tracking regulatory requirements, and generating required reports
- Treasury management, optimising cash management, coordinating payments
AI agents for insurance
- Claims processing, verifying documentation, detecting potential fraud, processing straightforward claims
- Risk assessment, analysing policyholder data, assessing risk factors, adjusting premium calculations
- Policy management, handling policy renewals, coordinating documentation
- Underwriting automation, evaluating applications, analysing risk factors, and automatically generating policy terms
- Managing policyholder inquiries and assigning complex issues to appropriate specialists
AI agents for logistics
- Route optimisation, analysing traffic patterns, weather conditions, and delivery schedules, adjusting delivery sequences in real-time.
- Warehouse automation, coordinating robotic systems, managing inventory placement, optimising picking sequences
- Fleet management, scheduling maintenance, coordinating repairs
- Tracking shipments, predicting delays, automatically initiating contingency plans
- Last-mile delivery coordination, managing delivery schedules, coordinating driver assignments
AI agents for manufacturing
- Production line optimisation, monitoring equipment performance, adjusting production parameters
- Quality control automation, inspecting products, adjusting production parameters to maintain quality standards
- Inventory management, tracking raw materials, coordinating supplier orders
- Monitoring machine health, predicting maintenance needs, scheduling preventive maintenance
- Production scheduling, coordinating resource allocation
AI agents for automotive
- Assembly line optimisation, coordinating robotic systems, monitoring quality metrics, adjusting production parameters
- Tracking component inventory, managing supplier relationships, coordinating delivery schedules
- Monitoring vehicle performance, detecting potential issues, scheduling maintenance based on predictive analytics
- Verifying assembly accuracy, adjusting production processes to maintain quality standards
- Managing parts inventory, coordinating supplier orders, maintaining optimal stock levels across dealership networks
AI agents for oil, gas and mining
- Tracking equipment performance, predicting maintenance needs
- Production optimisation, monitor extraction processes, optimising resource utilisation
- Monitoring safety metrics, detecting potential hazards, initiating safety protocols
- Tracking environmental indicators, detecting anomalies, coordinating response actions
- Resource management, optimising extraction schedules, coordinating equipment deployment
An example use case for
a multi-agent AI system
This multi-agent AI system (MAS) automates responses to RFI/RFPs by leveraging several LLM-based agents, considering available specialists, previous responses, and estimates.
Raw resumes (from external sources) are received and passed through the Sensitive Info adapter to filter out confidential data.
RFIs (Requests for Information) arrive from different sources (mailbox, MS Forms, web interface). Since this is typically public information, the sensitive adapter is unnecessary.
The LLM-based agent (information retrieval, structured output) processes resumes and extracts structured details.
The structured data is then stored in our Internal Resumes DB for future matching and retrieval.
An LLM-based agent (information retrieval, topic modelling, similarity search, RAG, and instruction-based decision-making) decomposes requests into meaningful parts and matches chunks with the internal DB, which contains historical responses/cases/experience and their vector representations.
It generates assignment tickets for SMEs to verify, refine, or complete missing information if needed.
Another LLM-based agent (semantic matching and similarity) matches the RFI requirements with relevant candidates' skillsets from the Internal Resumes DB (which contains the outputs from the previous agent).
A separate LLM-based agent (estimation – regression task, where the underlying NLP+ML estimator is used) inputs costs and effort based on historical information.
Both agents are used with the sensitive information adapters to ensure compliance before final output.
The commercial response is compiled, incorporating all retrieved data, estimations, and team composition.
SMEs validate and refine the final response before submission.
The finalized proposal is prepared and sent to the client (an additional conversational agent can also cover the mailing/communication flow).
