Before any agent can operate effectively, your data needs to be accessible, structured, and trustworthy. Most organisations have valuable data locked in ERP systems (SAP, Oracle), MES platforms, legacy databases, or paper. Phase 1 breaks that open — safely and systematically.

• AI opportunity assessment — map your highest-value, highest-feasibility agent use cases with domain SME input, not generic AI hype
• Data landscape audit — identify and prioritize the 2–3 data sources tied to your first target use case (e.g. production logs, inventory levels, transaction records)
• Cloud data layer setup — AWS S3 or Google Cloud Storage as staging, with a serverless warehouse (BigQuery or Redshift Serverless) for querying. You don't need to move everything — just what matters
• Governance first — data access controls, security perimeters, and compliance boundaries established before any model touches your data
• Define success metrics — measurable ROI thresholds signed off upfront, so Phase 2 has clear pass/fail criteria and executive alignment

Pick the single most painful, repetitive task your team performs. Our SMEs validate the selection and ROI model before a line of code is written. The three highest-return starting points across industries:

• Anomaly Monitoring Agent — reads sensor, PLC, or transactional data; uses an LLM to classify deviations and pushes alerts to Slack, Teams, or email. No one stares at dashboards. The output is low-risk, making it the ideal first deployment.
• Inventory / Reorder Agent — pulls stock levels on a schedule, compares against reorder thresholds, and drafts purchase order communications for human approval before dispatch
• Shift Report / Narrative Generation — ingests raw operational data and auto-authors daily or weekly management reports. A task consuming 30–60 minutes per shift becomes a 2-minute review cycle

Infrastructure footprint to get started:

• A single Linux VM (AWS EC2 t3.small or GCP e2-standard-2) running a Python task scheduler — cron or lightweight Airflow — is sufficient to begin
• Add a vector database (Pinecone, Weaviate, or pgvector on Postgres) if the agent needs to reference internal documents such as SOPs, compliance manuals, or runbooks

Once Phase 2 agents are live and ROI is measurable, you invest in orchestration. Individual agents become a collaborative network — handing work to each other, escalating exceptions, and making compound decisions that no single agent could handle alone.

• Multi-agent orchestration — using LangGraph, CrewAI, or Anthropic's native tool-use and multi-step reasoning: the anomaly agent triggers the inventory agent, which triggers the supplier email agent, which escalates to a human approver only when thresholds are breached
• Enterprise RAG knowledge base — a Retrieval-Augmented Generation layer so agents answer questions grounded in your actual manuals, compliance documentation, regulatory frameworks, and runbooks — not general LLM hallucination
• Agent performance monitoring — drift detection, prompt regression testing, and SME validation cycles to ensure agents remain accurate as your business evolves
• Governance & audit layer — complete decision logging, explainability reporting, and human-in-the-loop override mechanisms for high-stakes automated actions