Using LangChain and Arcade.dev to Build AI Agents For Consumer Packaged Goods: Top 3 Use Cases

Key Takeaways

• CPG companies hit a multi-user authorization wall, not a capability gap: Most agent projects stall in production because leaders can’t safely govern what permissions and scopes an agent has after it’s logged in across fragmented, domain specific systems (ERPs, retailer portals, communications). Arcade.dev’s MCP runtime replaces months of custom permissioning, token/secret handling, and auditability work.
• Weather-based demand forecasting delivers fastest ROI: Unilever achieved 30% sales increases in temperature-sensitive categories by connecting AI agents to weather APIs and production systems — a use case CPG teams can validate within 90 days
• Trade promotion optimization recovers wasted spend: CPG companies allocate 15-25% of revenue to trade promotions, yet most campaigns fail to generate positive ROI — AI agents analyzing competitive activity, seasonality, and historical lift deliver higher incremental volume from coordinated promotions
• Start with one use case, then scale: Successful CPG implementations begin with a single product category or workflow, prove value within 90 days, then expand incrementally
• Multi-agent orchestration transforms supply chain coordination: Specialized agents for demand sensing, inventory optimization, and route planning working together reduce transportation costs and stockouts through improved coordination

Here's what most CPG companies get wrong about AI agents: they build impressive proof-of-concepts demonstrating what's possible, then hit a multi-user authorization wall when deploying to production. The gap isn't technical capability — it's the unsolved problem of letting AI agents securely act on behalf of multiple users across fragmented, domain-specific enterprise systems like SAP, Oracle SCM, retailer portals, and dozens of communication platforms.

Arcade.dev closes this gap as the MCP runtime that enables and governs multi-user authorization across tools—so agents operate with delegated, scoped permissions tied to each user, rather than broad system accounts. When your LangChain agent needs to read sales data from Salesforce, send promotional updates via Gmail, coordinate teams through Slack, and query proprietary demand forecasting models — Arcade handles the delegated user authorization and scoped permissions that make these actions safe, auditable, and compliant.

This matters to three groups at once: AI/ML teams stop burning cycles on permissioning plumbing and can focus on agent behavior and outcomes; security teams get consistent scoped access controls and auditable actions aligned to existing identity; and business leaders get agents that can take real, accountable actions across core systems—faster, with less organizational risk.

Building these agents without Arcade means rebuilding multi-user authorization infrastructure for every tool: delegated scopes per user, token/secret lifecycle handling, revocation edge cases, and action-level auditability—repeated across every critical system. That’s months of work before the first production workflow can safely take real actions.

What Are AI Agents and Why CPG Companies Need Them Now

AI agents differ fundamentally from chatbots: chatbots respond to queries, while agents take autonomous actions on behalf of users. In CPG contexts, this means an agent doesn't just answer "what are my current inventory levels?" — it reads your warehouse management system, checks incoming shipments, identifies items approaching reorder points, and creates purchase orders with approved suppliers.

This distinction matters because CPG operations run through manual, error-prone workflows distributed across fragmented, domain-specific systems. Supply chain managers toggle between demand planning tools, supplier portals, logistics platforms, and communication applications. Brand managers navigate promotional calendars, retailer POS data, competitive intelligence feeds, and marketing automation systems. Sales teams coordinate across CRM platforms, email, calendar applications, and order management systems.

AI agents collapse these fragmented, domain-specific workflows into conversational interfaces backed by authenticated tool access. The business case is simple: significant reduction in routine tasks through automation, freeing teams to focus on strategic decisions rather than administrative coordination.

But deployment requires solving multi-user authorization at scale. When an AI agent acts, it needs:

• Delegated user permissions — not system-level admin access that bypasses controls
• Scoped tool access — reading inventory data doesn't grant permission to delete records
• Just-in-time authentication— users approve sensitive actions before execution
• Audit trails — every agent action tracked for compliance and accountability
• Token security — credentials never exposed to the LLM itself

Traditional chatbots avoid these requirements by staying read-only and advisory. Production CPG agents require write access to mission-critical systems — which is why multi-user authorization becomes the primary barrier blocking deployment.

Why Multi-User Authorization Matters for CPG AI Deployments

CPG AI agents operate across multiple retailers, suppliers, and internal teams where different users need different access levels to the same systems. A regional sales manager might access promotional data for their territory. A national account manager sees enterprise-wide figures. A procurement specialist queries supplier pricing but can't modify promotional calendars.

The multi-user authorization challenge compounds when agents need to act across multiple user contexts. A trade promotion agent serving 50 account managers across 200 retail partners requires secure access to each manager's CRM credentials, promotional multi-user authorization levels, and retailer-specific data agreements — without storing persistent tokens or granting blanket system access.

Building this infrastructure from scratch forces CPG development teams into problems outside their core expertise:

• Implementing OAuth flows for Gmail, Slack, Salesforce, and custom enterprise systems
• Managing token refresh, expiration, and revocation across hundreds of users
• Scoping permissions so agents access only what each user has authorized
• Maintaining compliance documentation for every multi-user authorization pattern
• Handling edge cases when users leave the organization or revoke access

Teams attempting custom multi-user authorization solutions typically burn 6-12 months before shipping their first production agent — time competitors using Arcade's MCP runtime spend refining agent intelligence and business value.

How LangChain and Arcade.dev Work Together to Enable Secure AI Agents

LangChain has emerged as the leading framework for building AI agents, with broad adoption across CPG companies. The framework excels at chaining LLM-driven tasks, managing retrieval workflows, and orchestrating multi-step agent reasoning. LangGraph — the graph-based state management layer built on LangChain — introduces conditional logic and decision points that let agents handle complex workflows like demand forecasting or multi-source promotional analysis.

Arcade.dev serves as the MCP runtime that enables and governs agent authorization across tools, integrating with LangChain for secure tool execution. While LangChain handles agent orchestration and reasoning, Arcade manages the critical infrastructure that lets agents safely interact with real-world systems.

LangChain's Role: Agent Orchestration and Reasoning

LangChain enables CPG AI agents to decompose complex tasks into manageable steps, maintain context across multi-turn conversations, and coordinate specialized sub-agents for different workflow components.

For a demand forecasting agent, LangChain might orchestrate:

• Query decomposition: Breaking "predict ice cream demand for the Southwest region next week" into specific data retrieval and analysis steps
• Parallel data retrieval: Weather forecasts, historical sales patterns, promotional calendars, competitor activity
• Result aggregation and pattern recognition
• Forecast generation with confidence intervals
• Recommendation delivery with supporting rationale

The framework's strength is orchestration, not multi-user authorization. LangChain assumes tools are already accessible and correctly scoped. This works for internal demos but fails in production multi-user environments where different supply chain managers need different access levels to the same systems.

Arcade's Role: Secure Tool-Calling and Multi-User Authorization

Arcade solves the multi-user authorization gap by serving as the MCP runtime between LangChain agents and the tools they need to access. When a CPG AI agent calls a tool, Arcade:

1. Validates multi-user authorization: Confirms the user has granted the agent permission for this specific tool and scope
2. Retrieves scoped tokens/secrets: Pulls only what’s required for the approved action
3. Executes the tool call: Runs the action on behalf of the user within the approved scope
4. Records the action: Produces a complete audit trail of what the agent did
5. Returns results: Sends tool output back to the agent without exposing tokens or secrets

This zero-token-exposure approach means the model never sees tokens or secrets—only tool capabilities and tool results.

Arcade's tool catalog provides pre-built connectors for Gmail, Slack, Salesforce, HubSpot, and other platforms CPG companies use daily. For proprietary systems — ERPs, warehouse management platforms, retailer portals — teams use Arcade's custom SDK to wrap internal APIs as authenticated agent tools without rebuilding authorization infrastructure.

Tools can also be built and used immediately via Arcade’s MCP tool framework—they don’t need to appear in the tool catalog to be usable in production workflows.

Use Case 1: Weather-Based Demand Forecasting for Temperature-Sensitive Categories

Ice cream, beverages, and other temperature-sensitive products experience dramatic demand swings based on weather, but traditional planning systems react too slowly, causing stockouts or waste. A significant temperature increase can raise ice cream sales in a region — information available 14 days in advance that most CPG planning processes can't incorporate quickly enough.

The business impact is substantial. Unilever achieved 30% sales increases in key markets by integrating weather data into AI-driven demand forecasting. Nestlé reported 30% reduction in demand forecasting errors through similar approaches. These outcomes stem from AI agents that can actually act on weather insights — adjusting production schedules, updating inventory positions, and coordinating distribution — rather than just surfacing recommendations humans must manually implement.

How the Demand Forecasting Agent Works

The LangChain agent orchestrates a workflow connecting multiple data sources through Arcade's secure tool access:

• Weather data retrieval: Pulling 14-day forecasts from weather APIs, correlating temperature patterns with historical demand
• Sales history analysis: Querying ERP and POS data to identify SKU-level sensitivity to temperature changes
• Production system integration: Reading current production schedules and capacity constraints
• Inventory positioning: Checking warehouse levels and distribution center capacity
• Action execution: Generating production adjustment recommendations, updating forecasts, alerting supply chain teams

The agent doesn't just analyze — it acts. When temperature forecasts indicate a heatwave, the agent can update demand forecasts in the planning system, generate production schedule modifications, and send coordination messages to plant managers and logistics teams.

Without Arcade, building this workflow requires custom authorization for each system: weather API credentials, ERP OAuth flows, warehouse management system tokens, email service authentication. Each integration consumes development time and creates maintenance burden. With Arcade, the agent accesses each system through the user's delegated permissions, maintaining appropriate access controls and audit trails.

Starting with a 90-Day Pilot

CPG teams should begin with a focused pilot rather than attempting enterprise-wide deployment. A typical starting approach:

• Select one product category: Ice cream, beverages, or another temperature-sensitive product line
• Deploy in suggestion mode: Agent generates recommendations, humans approve actions initially
• Run parallel with existing processes: Validate agent forecasts against current methods for 4-8 weeks
• Measure and iterate: Track forecast accuracy, stockout reduction, waste reduction

This focused approach addresses the reality that 48% of CPG companies delay AI projects waiting for complete data integration. Start with available data sources, prove value, then expand data connectivity incrementally.

Use Case 2: Trade Promotion Optimization and Sales Coordination

CPG companies allocate 15-25% of revenue to trade promotions, but most campaigns fail to generate positive ROI. The challenge is distinguishing truly incremental volume from time-shifted purchases — did that promotional discount actually drive new sales, or did consumers simply stock up and buy less later?

AI agents analyzing promotional pricing, competitive activity, seasonality patterns, and historical lift metrics simultaneously deliver higher incremental volume from coordinated promotions. The key is connecting insights to action: agents that can not only analyze promotion effectiveness but also update promotional calendars, coordinate with retail partners, and adjust pricing strategies.

Connecting Sales Data Across Retailer and CRM Systems

Trade promotion optimization requires access to data fragmented across multiple systems:

• Retailer POS data: Actual sales velocity during and after promotions
• Competitive intelligence: Nielsen/IRI data on competitor promotional activity
• CRM systems: Account-level promotional commitments and negotiated terms
• Financial systems: Trade spend budgets and actual costs
• Communication platforms: Coordination with retail buyers and internal stakeholders

Arcade's Salesforce and HubSpot integrations provide secure access to CRM data, while custom tool development enables connection to proprietary retailer portals and category management platforms. The agent operates with each sales manager's credentials, respecting their existing access permissions rather than requiring new system-level accounts.

The Promotion Coordination Agent Workflow

A trade promotion agent built on LangChain with Arcade multi-user authorization orchestrates:

• Historical analysis: Querying past promotion results by retailer, product, timing, and competitive context
• Lift prediction: Modeling expected incremental volume for proposed promotions
• Calendar coordination: Checking promotional schedules for conflicts and synergies
• Budget tracking: Monitoring trade spend against allocated budgets
• Communication: Sending promotional updates to retail partners, coordinating with field sales teams

The Arcade Chat platform demonstrates this multi-turn agent capability — handling real work across connected services through natural conversation. Sales managers interact conversationally: "Show me our Q3 promotional performance at Kroger, then draft a proposal for Q4 that improves incrementality." The agent retrieves data, performs analysis, and prepares communications — all within the user's authorization boundaries.

Use Case 3: Supply Chain Coordination Through Multi-Agent Orchestration

Fragmented, domain-specific systems prevent end-to-end supply chain visibility. Production schedules don't reflect promotional spikes. Logistics can't anticipate weather disruptions. Inventory sits in suboptimal locations while other regions face stockouts. Manual coordination across systems creates delays and errors.

Multi-agent architectures address this complexity by deploying specialized agents for different functions, coordinated through LangGraph workflows:

• Demand sensing agent: Monitors sales signals, weather, social media, competitive activity
• Inventory optimization agent: Balances stock levels across distribution network
• Route planning agent: Optimizes logistics for cost and service level
• Promotional coordination agent: Aligns supply chain capacity with marketing plans

This orchestrated approach delivers measurable outcomes through improved coordination and reduced manual intervention.

Building Agent Handoffs for Complex Supply Chain Workflows

Multi-agent systems require careful coordination when specialized agents hand off work. When the demand sensing agent detects an unexpected spike, it must transfer context to the inventory optimization agent, which then coordinates with the logistics planning agent — each operating within appropriate multi-user authorization boundaries.

Arcade maintains consistent permissions across agent handoffs. Each agent accesses only the systems its function requires, with user-level authorization ensuring appropriate access controls. The demand sensing agent reads POS data but can't modify production schedules. The logistics agent updates route plans but can't change promotional commitments.

The Archer Slack agent demonstrates this pattern for team coordination — an AI agent that lives in Slack workspaces, accessing Gmail, Google Calendar, and other services to coordinate across tools while respecting user permissions. For supply chain teams, similar agents monitor Slack channels for escalations, surface relevant data from connected systems, and take coordination actions when authorized.

Integrating Internal Communication with Supply Chain Actions

Supply chain disruptions require rapid cross-functional response. When a supplier misses a delivery window, teams need immediate visibility and coordinated action across procurement, production, logistics, and customer service.

AI agents with Gmail and Slack integration can:

• Monitor email for supplier communications indicating delays
• Surface disruption alerts in relevant Slack channels
• Query alternative supplier availability and pricing
• Draft communication to affected customers
• Update production schedules to reflect material availability

Building an AI agent for Gmail shows this pattern in action — agents that read and send emails with secure authenticated access, integrating email workflows with broader business processes.

Building Secure and Compliant CPG AI Agents with Arcade.dev

CPG AI agents handle sensitive data: proprietary formulations, competitive pricing strategies, retailer agreements, and promotional calendars. Security failures create competitive disadvantages and legal liability. Compliance isn't optional — it's the prerequisite for production deployment.

The security challenge compounds when agents need broad system access to be effective. A trade promotion agent requires access to pricing data, promotional calendars, CRM records, and retailer communications. Traditional security models grant system-level access to applications, creating attack surfaces and compliance gaps when agents need to act on behalf of many users with different permission levels.

Zero Token Exposure Architecture

Arcade's architecture eliminates credential exposure risk through strict separation between reasoning and execution:

• Agent requests tool execution but doesn't have credentials
• Arcade validates multi-user authorization and confirms user permission for this specific tool
• Arcade retrieves encrypted credential with appropriate scope
• Arcade executes action on behalf of the user
• Arcade returns results without exposing credentials

At no point do credentials enter the LLM context. The agent sees only tool definitions (what actions are possible) and tool results (what happened), never the multi-user authorization tokens required to execute actions.

With SOC 2 Type 2 certification, Arcade.dev becomes the authorized path to production with these key points: Just-in-time authorization validated by independent auditors. Tool-level access controls that inherit from existing identity providers. Complete audit trails for every agent action. VPC deployment options for air-gapped environments.

Data Protection for Sensitive CPG Information

CPG companies protect proprietary formulations, competitive pricing, and retailer-specific agreements as trade secrets. AI agents accessing this information must maintain the same confidentiality controls as human users.

Arcade does NOT handle underlying CPG data — it manages tokens and secrets for multi-user authorization. This distinction matters: sensitive business data flows directly between the agent and source systems, while Arcade focuses on token/secrets management; tool executions are mediated by the runtime and may be logged per your configuration. Arcade provides the secure credential management that enables access, not data storage or processing.

For CPG deployments requiring OAuth integration with internal systems, Arcade connects to existing identity providers (LDAP, Active Directory, Okta), ensuring agent permissions align with established access control policies.

Frequently Asked Questions

How do CPG companies handle retailer data partnerships required for AI agent effectiveness?

Retailer POS data is critical for accurate demand forecasting and promotion optimization, but securing data-sharing agreements takes 3-6 months on average. CPG teams should initiate partnership discussions early in parallel with technical implementation. Use shipment data as an initial proxy — tracking what you ship to distribution centers provides directional demand signals while retailer partnerships are established. Once POS data access is secured, Arcade's multi-user authorization framework enables secure integration with retailer portals through custom tool development, respecting data-sharing agreement terms and access restrictions.

What cross-functional team structure works best for CPG AI agent implementations?

Successful CPG AI agent projects require alignment across three groups: business domain experts (supply chain, sales, marketing) who define use cases and validate outcomes; IT/development teams who build integrations and manage technical infrastructure; and security/compliance teams who ensure multi-user authorization patterns meet corporate policies. The most effective approach assigns a dedicated cross-functional team to the initial pilot use case rather than distributing responsibility across existing organizational silos. This team should include decision-making authority to resolve conflicts between speed-to-value and compliance requirements.

How should CPG companies measure ROI on AI agent investments?

Focus metrics on business outcomes rather than technical capabilities. For demand forecasting agents, track forecast accuracy improvement (measured against historical baselines), stockout reduction, and waste reduction from overproduction. For trade promotion agents, measure incremental volume lift (comparing agent-optimized promotions against historical averages) and trade spend efficiency. For supply chain coordination, monitor transportation costs, inventory carrying costs, and order fulfillment cycle times. Establish baselines during the parallel-running phase of your 90-day pilot before transitioning to full automation.

Can AI agents integrate with legacy ERP systems that lack modern APIs?

Yes, though integration complexity varies. Many legacy ERPs expose data through database connections, file exports, or older SOAP APIs rather than modern REST interfaces. Arcade's custom tool SDK supports these integration patterns — development teams can build tools that query databases directly, parse file exports, or call legacy web services. The multi-user authorization layer works identically regardless of underlying integration method. Teams should budget additional development time for legacy systems (days rather than hours) but the pattern remains consistent: wrap existing access methods as authenticated tools that inherit user permissions.

What happens when an AI agent encounters a multi-user authorization failure mid-workflow?

Arcade's just-in-time authorization model handles multi-user authorization failures gracefully. When an agent attempts an action the user hasn't authorized, Arcade returns an authorization challenge rather than failing silently. The agent can present this challenge to the user, who then grants permission for that specific tool. For multi-step workflows, this means agents can request multi-user authorization incrementally as needed rather than requiring comprehensive upfront permissions. This pattern also handles cases where user permissions change during a workflow — if access is revoked, the agent receives a clear multi-user authorization failure and can escalate to human review rather than proceeding with stale credentials.