Corona Mobile Application Development

Table of Contents Toggle OVERVIEWBACKGROUND & CHALLENGEMETHODOLOGYTARGET IMPACT OVERVIEW Initiative: Research programme on agents that adapt, evolve and optimise their own reasoning over time Domain: Multi-agent systems and agent memory Scope: Multi-tier long-term memory, learned inter-agent communication, self-improvement loop Expertise: Graph Neural Networks · Agent Memory · LLM Agents Stage: In build BACKGROUND & CHALLENGE Most agent systems today route every message through a central hub and treat memory as a single flat store. Scaling the underlying LLM brings diminishing returns in coordination and continual learning. SAGE tests whether memory quality and learned communication, rather than model size, are the real levers of autonomous performance. METHODOLOGY Agents modelled as nodes in a graph, with edges learned per task rather than configured Graph attention communication layer that routes messages and picks a protocol per message Multi-tier memory: scratch-pad, vector store, knowledge graph and long-term archive Consolidation and eviction policies that keep memory useful as it grows Memory-as-a-Graph linking interactions, queries and derived insights Self-improvement through experience replay and in-context algorithm distillation TARGET IMPACT Better sample efficiency Fewer episodes for an agent to reach competence. Leaner coordination Fewer messages per solved task, with no hub in the path. Continual learning Lower forgetting as agents move across tasks. Graceful degradation Losing one agent reroutes work instead of stopping the system.

Table of Contents Toggle OVERVIEWTHE CHALLENGESCOPE OF WORKOUTCOMES OVERVIEW Product: Vision assistant for garment inspection on the inspector’s tablet and at the site edge Domain: Apparel quality control, inspection services Scope: Technical assessment, prototypes by milestone, full roll-out across sites Expertise: Computer Vision, Edge AI, MLOps, Label Quality, Offline-first Systems Engagement: Three engagements over three years, each with its own acceptance gate THE CHALLENGE A global inspection services company photographs every garment defect its inspectors find, and holds 13 TB of images across 120,000 inspections. The archive cannot be used as it is: a pointer sticker sits in almost every photo, severity varies between inspectors, older tablets stored downscaled images, and no defect-free examples were ever captured. The company wanted suggestions on the inspector’s screen within two seconds, at sites with poor or no connectivity, while keeping the final decision with the inspector. SCOPE OF WORK Technical assessment of the archive, devices and workflow, fixing scope and price on evidence Marker handling pipeline removing the sticker from training data, with release tests on every model Two-stage vision model: region proposer and native-pixel crop classifier with a no-defect class Severity as a lookup in the customer’s code-by-zone matrix, never a model output Offline-first deployment: int8 model on the tablet, site edge appliance, private cloud tenant Prototype at each milestone, then staged roll-out with signed bundles and automatic rollback OUTCOMES Suggestion in under 2 s On the existing tablets, with no network. One severity for one defect Model gives type, zone and size, the customer matrix gives severity. Sticker removed from learning Release tests show chance accuracy on marker-only crops. Everything owned by the customer Models, code and data in open formats, in their own tenant.

Table of Contents Toggle OVERVIEWTHE CHALLENGESCOPE OF WORKOUTCOMES OVERVIEW Product: An agentic chatbot for patients and staff grounded in the governed platform for patient-facing Domain: Healthcare Scope: Multi agent orchestration, Azure OpenAI, RAG on AI Search, Epic FHIR actions Expertise: Agentic AI, RAG, LLM Ops, Responsible AI Engagement: Second of two, building intelligence on the data foundation THE CHALLENGE With governed data in place from the first engagement, the network wanted a conversational assistant that answers patient and staff questions with citations, checks appointment availability and starts bookings in Epic. It had to stay inside HIPAA boundaries, redact PHI and escalate to humans when confidence is low. SCOPE OF WORK Multi agent design with a planner agent routing to retrieval, action and analytics agents Azure OpenAI GPT 4o reasoning with structured tool calling and guardrails RAG over Azure AI Search vector indexes built nightly from the Gold layer and clinical content Epic on FHIR action tools for slot lookup, appointment booking and rescheduling Conversation memory in Cosmos DB with PHI redaction and Content Safety on every turn Evaluation harness, tracing and human in the loop review before responses ship OUTCOMES 63%+ inquiries self served Patients resolve routine questions without staff involvement. Under 3s median response Grounded answers with citations to governed sources. 28% fewer no shows Proactive rescheduling driven by agent actions on Epic. Zero PHI incidents Content Safety and redaction enforced on every turn.