AI agents are software systems that can interpret goals, reason over context, call tools, use data, coordinate with other agents and return useful work products under defined controls. For Orange Analytics, agentic AI is not a novelty layer on top of an application. It is a software design discipline that connects language models, business rules, data retrieval, APIs, workflow orchestration, security controls and measurable delivery outcomes.
We help organisations design, develop, integrate and operate AI agent solutions that are useful, observable and governed. The aim is to move beyond isolated prompts into dependable agentic workflows that can support research, analysis, customer operations, internal knowledge work, software delivery, reporting and decision-support processes.
What We Build
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AI Agent Strategy and Solution Design
We identify where agents can create practical value, define the target workflow, map human approval points, select the right architecture and document the constraints that keep the system reliable.
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Orchestrated Multi-Agent Workflows
We design planner, researcher, analyst, builder, reviewer and support-agent patterns that can collaborate through clear handoffs, shared context and controlled tool access.
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LLM Application Integration
We integrate models with business applications, APIs, databases, document repositories, search systems and reporting layers while keeping credentials, permissions and audit trails separate from prompts.
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Retrieval and Knowledge Systems
We use retrieval-augmented generation, structured knowledge bases, vector search and metadata filtering so agents can work from approved business information rather than unsupported assumptions.
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Evaluation, Guardrails and Governance
We define test sets, expected behaviours, escalation rules, content boundaries, confidence handling, logging and review workflows so agent behaviour can be assessed before and after release.
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Production Delivery and Support
We package agentic workflows as maintainable software, monitor quality and cost, support iteration, and help teams understand how to operate AI systems responsibly.
Agentic Software Design Principles
Effective AI agent software starts with a clear boundary between model judgement and system authority. Agents may interpret requests, break down tasks, draft outputs or propose actions, but production systems still need deterministic rules for permissions, data access, validation, approval and failure handling.
Our design approach considers model selection, prompt architecture, tool schemas, retrieval quality, memory scope, data lineage, fallback behaviour, observability, cost controls and human-in-the-loop review. This helps reduce the risk of brittle demos and supports systems that can be tested, maintained and improved over time.
Delivery Approach
- Discover: define business outcomes, agent boundaries, source systems, risk levels and success measures.
- Design: model the agent workflow, tool contracts, retrieval strategy, permissions, evaluations and user experience.
- Develop: build the agent services, integrations, prompts, tool adapters, RAG pipelines and approval flows.
- Evaluate: test task completion, factual grounding, safety boundaries, latency, cost and regression behaviour.
- Deploy: release through controlled environments with logging, monitoring, rollback and human escalation paths.
- Improve: review production traces, feedback, data quality and business outcomes to refine the system responsibly.
Where AI Agents Fit
Agentic systems can assist with document-heavy analysis, internal knowledge assistants, workflow triage, report generation, software delivery support, data quality review, operational monitoring, customer-service preparation and decision-support tasks. The strongest opportunities usually appear where work is repeatable, information-rich and still benefits from human judgement at key approval points.
Orange Analytics can support new AI-agent initiatives as standalone solutions or integrate agentic capabilities into existing web applications, mobile systems, data analytics platforms and internal business tools.
Ready to Explore AI Agents?
We can help you assess candidate workflows, define the architecture, build a proof of value and move towards a governed production implementation. The result is a practical AI capability designed around your data, systems, users and operational controls.