Build Your AI Context Stack
Get the blueprint for implementing context graphs across your enterprise. This guide walks through the four-layer architecture—from metadata foundation to agent orchestration—with practical implementation steps for 2026.
Get the Stack GuideHow are AI agents being used in manufacturing? An overview
Permalink to “How are AI agents being used in manufacturing? An overview”According to McKinsey, manufacturers have experienced a 20% drop in inventory and logistic costs with agentic AI. Deployments already in production cover a wide range of operations:
- Predictive maintenance: Agents monitor vibration, temperature, and current draw from equipment sensors, correlate trends against historical failure patterns, and trigger work order recommendations before a breakdown occurs.
- OEE monitoring and alerting: Agents track availability, performance, and quality metrics across production lines in real time, surfacing deviations before they compound into larger losses.
- Quality inspection: Vision-based agents classify defects on the line, correlating inspection outcomes with upstream process variables to identify root causes without waiting for end-of-shift review.
- Production scheduling: Agents analyze order backlogs, machine capacity, material availability, and shift constraints to generate or adjust schedules, recommending changes that a planner reviews before execution.
- Energy optimization: Agents monitor per-unit energy consumption against production volume, flagging lines where consumption is drifting outside established bounds.
- Supplier communication: Agents generate reorder requests, flag supplier performance trends, and draft escalation summaries grounded in verified purchase history and contract terms.
- SOP lookup and compliance support: Associates and engineers query agents for the current version of a standard operating procedure, with the agent surfacing the relevant section and its last-approved date.
Siemens has deployed its Industrial Copilot, built in collaboration with Microsoft, across its own factories and at customer sites. At Automate 2025 in Detroit, Siemens announced an expansion from copilots to agents: semi-autonomous systems capable of executing complete industrial workflows end-to-end across the value chain.
Mercedes-Benz has partnered with Apptronik to evaluate humanoid robots for vehicle assembly, exploring how they can handle tasks that require dexterity and adaptability on the line.
What connects these deployments is the gap between what the demo showed and what production requires. To be effective at scale, AI agents for manufacturing need governed data infrastructure tagged with reliability metadata, and lineage-traced to the alerts it produces.
The OT/IT fragmentation problem: Why manufacturing is a uniquely difficult context environment
Permalink to “The OT/IT fragmentation problem: Why manufacturing is a uniquely difficult context environment”Manufacturing data lives across MES, SCADA historians, ERP, QMS, PLM, and IoT platforms, each built at different times, with different naming conventions and no shared semantic layer.
An agent querying “OEE for Line 3” may get three different numbers: the MES calculates availability x performance x quality; the ERP divides actual output by theoretical capacity; the historian returns a pre-calculated shift aggregate configured years ago by a contractor.
This fragmentation compounds across systems and agents, with no traceable lineage to the raw data behind them. A governed manufacturing context layer with canonical definitions for OEE, scrap rate, MTBF, MTTR, and energy per unit is not optional infrastructure. It is the prerequisite for any agent to produce an answer an operator can trust.
Not every agent deployment requires the same depth of governance, though. The right governance structure depends on who the agent serves, what it can act on, and how much error the use case can tolerate. That is where the three-tier model becomes useful.
Tier 1: Monitoring and alerting agents
Permalink to “Tier 1: Monitoring and alerting agents”Monitoring and alerting agents watch OEE, quality yield, energy consumption, and maintenance signals continuously, surfacing deviations that require attention. They carry the highest adoption in production today because operators already use dashboards and these agents work within that familiar pattern.
Context requirements for Tier 1:
- Freshness SLAs per source: The context layer must encode how current each data source is at the moment of agent reasoning.
- Reliability tags on sensor feeds: Metadata encoding which feeds have calibration issues or pending maintenance, so agents escalate rather than act on suspect data.
- Threshold versioning: Alert thresholds change when product lines or materials change. The context layer must preserve which version was in effect when an alert was generated.
- Ownership assignment: Every monitored metric needs a named owner so escalation routing works correctly.
Tier 1 is where the governance baseline gets established. The certified data products, metric definitions, and lineage built here are what Tier 2 agents use for analysis.
Tier 2: Analysis and recommendation agents
Permalink to “Tier 2: Analysis and recommendation agents”Analysis and recommendation agents receive a deviation signal, or a direct query from an operator, and produce a reasoned recommendation: a root cause hypothesis, a corrective action, or a reference to the current SOP version. They operate at medium autonomy.
Context requirements for Tier 2:
- SOP and policy versioning: The agent must know which version of a procedure was current at the time of its recommendation.
- Lineage from sensor to KPI: When the agent cites a trend in a calculated metric, the lineage from raw sensor through the calculation must be traversable.
- Confidence signals: Recommendations grounded in degraded or unreliable input data must surface that distinction to the operator.
- Cross-system correlation context: Root cause analysis often requires connecting MES batch records, historian sensor trends, and QMS inspection outcomes across systems that were never designed to talk to each other.
Tier 3: Scheduling and action agents
Permalink to “Tier 3: Scheduling and action agents”Scheduling and action agents generate production schedules, dispatch maintenance technicians, communicate with suppliers, and in some configurations adjust process parameters.
The governance architecture required here builds directly on Tiers 1 and 2. Governed metric definitions, certified data products, versioned SOPs, and lineage-traced recommendations are all prerequisites. What Tier 3 adds is human-in-the-loop approval workflows and decision traces elevated from documentation to compliance artifacts.
Context requirements for Tier 3:
- Certified scheduling inputs: Order backlogs, machine capacity, and material availability must be pre-computed and versioned.
- Human-in-the-loop approval gates: Process parameter changes, line shutdowns for maintenance, and supplier escalations above defined thresholds require human sign-off before execution.
- Decision traces as compliance artifacts: Every action is linked to the data products, metric definitions, and policies that drove it, audit-ready by design.
- Policy nodes for action authorization: Establish which agent can take which action on which line, and under what conditions, enforced at the context delivery layer before execution.
Certified data products: The unit of trust for manufacturing AI
Permalink to “Certified data products: The unit of trust for manufacturing AI”Across all three tiers, agents must query certified data products rather than raw sensor streams or unvalidated ERP tables. A certified data product has a named owner, a defined freshness SLA, a quality certification record, versioned history, and lineage from raw source to output value.
Commonly certified products in manufacturing include OEE by line, scrap rate by product family, MTBF and MTTR by asset class, energy per unit by shift, and first-pass yield by batch. When these are certified, agent outputs can be trusted and traced. When they are not, the first confident-sounding wrong recommendation breaks operator trust in ways that persist long after the recommendation is corrected.
For Data Leaders Evaluating Where to Start
Atlan's CIO guide to context graphs walks through a practical four-layer architecture from metadata foundation to agent orchestration.
Get the CIO GuideWhy decision traces are critical to compliance architecture
Permalink to “Why decision traces are critical to compliance architecture”In regulated manufacturing, decision traces are the compliance foundation. Pharma (FDA 21 CFR Part 11), aerospace (AS9100), and automotive (AIAG) all have documentation requirements for process changes and decision records.
A complete decision trace links four things:
- The signal the agent acted on (data product, version, freshness timestamp).
- The rule or SOP version that authorized the action.
- The reasoning chain from input to output.
- The human approval that authorized execution.
This records why an event happened, what governed it, and who approved it, reconstructable years after the fact. Since decision traces cannot be bolted on after deployment, they must be built into the context layer from the start—which means the certified data products, policy nodes, and lineage infrastructure established in Tiers 1 and 2 are not just operational requirements.
How Atlan supports manufacturing AI agents in production
Permalink to “How Atlan supports manufacturing AI agents in production”Atlan enters as the governed context layer above the OT/IT stack, providing the shared semantic layer and governance infrastructure that makes agent recommendations traceable, policy-compliant, and operator-trustworthy.
Key capabilities for manufacturing AI include:
- An enterprise data graph: Connects MES, historians, ERP, QMS, PLM, and IoT platforms into a unified metadata layer. Plants, lines, work centers, assets, products, batches, and shifts are connected in one traversable graph with lineage from raw sensor data to calculated KPIs.
- Certified data products for agents: OEE, scrap rate, MTBF and MTTR, energy per unit, and first-pass yield are pre-computed, owner-assigned, and versioned with freshness SLAs. Tier 1, Tier 2, and Tier 3 agents query certified data products through Atlan’s MCP server.
- Decision traces for compliance: Every agent recommendation is linked to the specific data products, metric definitions, and policies that drove it.
- Policy layer for agent actions: Atlan’s AI Governance module enforces guardrails at the context delivery layer, not hardcoded per agent implementation.
- Lineage-based quality propagation: A quality check failure on a raw sensor feed propagates downstream to the data product built on it. Agents see degraded quality signals before using a source.
- Context Engineering Studio: The workspace where manufacturing teams build, test, and validate the context layer before agents reach production.
Inside Atlan AI Labs & The 5x Accuracy Factor
Learn how context engineering drove 5x AI accuracy in real customer systems. Explore real experiments, quantifiable results, and a repeatable playbook for closing the gap between AI demos and production-ready systems.
Download E-bookReal stories from real customers in manufacturing
Permalink to “Real stories from real customers in manufacturing”How GM embeds trust with Atlan
Permalink to “How GM embeds trust with Atlan”"By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations."
— Sherri Adame, Enterprise Data Governance Leader, GM
How DigiKey built a unified, sovereign context layer for its data and AI estate
Permalink to “How DigiKey built a unified, sovereign context layer for its data and AI estate”"Atlan is our context operating system to cover every type of context in every system including our operational systems. For the first time we have a single source of truth for context."
— Sridher Arumugham, Chief Data Analytics Officer, DigiKey
Moving forward with AI agents for manufacturing
Permalink to “Moving forward with AI agents for manufacturing”The path to production-grade manufacturing AI agents follows the same sequence across all three tiers: build the governance infrastructure first, then build the agents.
Start with Tier 1. Establish certified data products for your primary KPIs, assign owners, define freshness SLAs, and build lineage from raw sources to agent-facing metrics. Use that infrastructure to deploy monitoring and alerting agents operators can see, question, and correct.
Move to Tier 2 once certified data products are stable. Build explainability into the context architecture before deployment, not after a wrong recommendation gets acted on. Tier 3 scheduling and action agents are the destination, requiring everything from Tiers 1 and 2 plus human-in-the-loop approval workflows and audit-ready decision traces.
FAQs about AI agents for manufacturing
Permalink to “FAQs about AI agents for manufacturing”What is a manufacturing AI agent?
Permalink to “What is a manufacturing AI agent?”A manufacturing AI agent is an autonomous or semi-autonomous system that perceives data from production systems, reasons over it, and takes action toward operational goals without step-by-step human direction. This ranges from monitoring OEE and alerting operators to deviations, to analyzing root causes and surfacing corrective recommendations, to optimizing production schedules and dispatching maintenance work orders.
What is the OT/IT fragmentation problem in manufacturing AI?
Permalink to “What is the OT/IT fragmentation problem in manufacturing AI?”The OT/IT fragmentation problem refers to the fact that manufacturing data lives across MES, SCADA historians, ERP, QMS, PLM, and IoT platforms, all with different naming conventions, different time resolutions, and no shared semantic layer. A term like “OEE” may be defined and calculated differently in each system. An AI agent that queries across these systems without a canonical definition layer will produce inconsistent outputs that operators cannot trust or validate.
Why are decision traces compliance artifacts in regulated manufacturing?
Permalink to “Why are decision traces compliance artifacts in regulated manufacturing?”In sectors governed by FDA 21 CFR Part 11, EU GMP Annex 11, AS9100, or IATF 16949, process changes and automated decisions must be documented with a record of what signal triggered the action, what rule or policy authorized it, and who approved it. An AI agent that recommends or executes a process change without generating this record produces an output that cannot be used in a regulated environment, regardless of whether the recommendation itself was correct.
What is a certified data product in manufacturing?
Permalink to “What is a certified data product in manufacturing?”A certified data product in manufacturing is a pre-computed, governed, versioned view of a key operational metric—such as OEE, scrap rate, MTBF, MTTR, or energy per unit—that has a named owner, a defined freshness SLA, a quality certification record, and full lineage from raw source data to the value the agent queries. Agents query certified data products rather than raw sensor streams or unvalidated ERP tables.
What does explainability mean for manufacturing AI agents?
Permalink to “What does explainability mean for manufacturing AI agents?”Explainability in manufacturing AI means that every recommendation an agent produces is traceable to the specific signal it acted on, the threshold or policy version it applied, and the data source and calculation logic behind the cited metric. Operators and process engineers with deep domain expertise will not act on recommendations they cannot interrogate.
How do the three tiers of manufacturing AI agents relate to each other?
Permalink to “How do the three tiers of manufacturing AI agents relate to each other?”The three tiers—monitoring and alerting, analysis and recommendation, and scheduling and action—are not independent deployment tracks. The governance infrastructure built for Tier 1 is exactly what Tier 2 agents depend on for explainable recommendations. The versioned SOPs, decision traces, and human-in-the-loop workflows established for Tier 2 are the prerequisite for Tier 3 deployments in regulated environments.
