Using LangChain and Arcade.dev to Build AI Agents For Pharmaceuticals & Biotech: Top 3 Use Cases

Key Takeaways

• Pharmaceutical AI agents unlock massive value but stall at production: The AI in biotechnology market will grow from $3.27 billion in 2024 to $18.76 billion by 2034, yet 45.8% of small companies cite performance quality as their top barrier to deployment , Arcade.dev's MCP runtime solves the multi-user authorization gap blocking production
• LangChain dominates but lacks production-ready authorization: 51% of organizations already have AI agents in production, with LangChain emerging as the leading framework , but without Arcade's delegated user authorization, these agents can't safely act across Gmail, Slack, EHRs, and lab systems
• AI could replace $140B in outsourced pharma services: The CRO market alone will grow from $70B in 2025 to $126B by 2034, creating a $350 billion opportunity for AI agents that can handle 55% of workforce hours
• Drug discovery leads adoption at 36% market share: Drug discovery and lead generation represents the largest AI application segment, with agents mining omics data, predicting ADMET liabilities, and proposing novel chemotypes , requiring secure access to molecular databases and literature repositories
• Biotech startups adopt faster than Big Pharma: While large pharma holds 52% market share, biotech startups show the fastest growth , implementing a single clinical trial coordination or literature review use case before scaling

Here's what most pharmaceutical and biotech companies get wrong about AI agents: they build impressive proof-of-concepts that demonstrate what's possible, then hit an authorization wall when trying to deploy 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.

Arcade.dev's AI tool-calling platform closes this gap by serving as the MCP (Model Context Protocol) runtime that enables and governs agent authorization across tools. When your LangChain agent needs to read clinical trial data from Salesforce, send patient appointment confirmations via Gmail, coordinate research teams through Slack, and query proprietary compound databases , Arcade handles the delegated user authorization and scoped permissions that make these actions safe, auditable, and compliant.

The market opportunity is staggering. AI agents could unlock $180-240 billion annually in the U.S. pharmaceutical industry alone. Yet 78% of organizations remain in the planning phase because performance quality concerns and security barriers prevent production deployment. For the pharmaceutical companies that solve authorization first, the competitive advantage compounds: faster drug discovery cycles, streamlined clinical operations, and reduced dependence on costly outsourced services.

Building these agents without Arcade means assembling custom OAuth flows for every service, managing token lifecycle and refresh logic, implementing fine-grained permission scoping, maintaining audit trails for regulatory compliance, and handling edge cases when user access is revoked , multiplied across dozens of enterprise platforms. Teams attempting this path typically spend 6-12 months on authorization infrastructure before writing their first production agent workflow.

What Are AI Agents and Why Pharmaceuticals & Biotech Need Them Now

AI agents differ fundamentally from chatbots: chatbots respond to queries, while agents take autonomous actions on behalf of users. In pharmaceutical contexts, this means an agent doesn't just answer "what are my upcoming clinical trial milestones?" , it reads your trial management system, checks team calendars, identifies scheduling conflicts, and sends coordination messages to site investigators.

This distinction matters because pharmaceutical R&D operates through manual, error-prone workflows distributed across fragmented systems. Clinical trial coordinators toggle between patient databases, email, calendar applications, regulatory submission portals, and investigator communication platforms. Drug discovery researchers navigate PubMed literature searches, compound databases, molecular modeling tools, and electronic lab notebooks. Manufacturing teams coordinate across quality management systems, supply chain platforms, equipment monitoring dashboards, and compliance documentation.

AI agents collapse these fragmented workflows into conversational interfaces backed by authenticated tool access. The business case is simple: AI could shoulder 55% of total biopharma workforce hours, freeing researchers and clinicians to focus on high-value judgment rather than administrative coordination.

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

• Delegated user permissions , not system-level admin access
• Scoped tool access , reading trial data doesn't grant permission to delete it
• Just-in-time authorization , users approve sensitive actions before execution
• Audit trails , every agent action tracked for regulatory compliance
• Token security , credentials never exposed to the LLM itself

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

Why Authentication and Security Matter in Pharma AI Deployments

Pharmaceutical AI agents operate in highly regulated environments where data breaches, unauthorized access, and compliance failures carry devastating consequences. FDA guidance emphasizes bulletproof audit trails, model retraining documentation, and bias mitigation. European EMA, Japanese PMDA, and other global regulators impose strict data residency and privacy requirements.

The authorization challenge compounds when agents need to act across multiple user contexts. A clinical trial coordination agent serving 50 investigators across 20 sites requires secure access to each investigator's email, calendar, and trial management credentials , without storing persistent tokens or granting blanket system access.

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

• Implementing OAuth 2.1 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 authorization pattern
• Handling edge cases when users leave the organization or revoke access

Teams attempting custom authorization solutions typically burn 6-12 months before shipping their first production agent , time competitors using Arcade's MCP-compatible platform 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 pharmaceutical and biotech 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 clinical trial coordination or multi-source literature 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

• Chains LLM calls for multi-step task decomposition
• Manages agent state and conversation context
• Routes decisions through conditional logic graphs
• Coordinates multi-agent collaboration workflows

Arcade's Role: MCP Runtime for Secure Tool Execution and Multi-User Authorization

• Provides OAuth-enabled access to Gmail, Slack, Salesforce, databases, and custom APIs
• Manages delegated user credentials without exposing tokens to LLMs
• Enforces scoped permissions and just-in-time approval workflows
• Maintains audit trails for regulatory compliance
• Handles token lifecycle, refresh, and revocation

This separation of concerns lets pharmaceutical teams focus on agent intelligence rather than authorization infrastructure. Instead of building custom OAuth flows for every enterprise system, developers use Arcade's pre-built connectors for common platforms and the custom SDK for proprietary lab systems or compound databases.

LangChain's Role: Agent Orchestration and Reasoning

LangChain enables pharmaceutical 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 drug discovery literature review agent, LangChain might orchestrate:

1. Query decomposition: Breaking "find novel EGFR inhibitors for NSCLC" into specific search strategies
2. Parallel literature searches across PubMed, clinical trial registries, patent databases
3. Result aggregation and relevance filtering
4. Citation extraction and knowledge graph construction
5. Summary generation with source attribution

LangGraph extends this with conditional decision points. When the agent encounters ambiguous results, it can route to human review. When it finds high-confidence matches, it proceeds automatically. This visual flow control with conditionals makes pharmaceutical workflows more transparent and debuggable , critical for regulatory validation.

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

Arcade's Role: Secure Tool-Calling and OAuth Abstraction

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

1. Validates user authorization: Confirms the user has granted the agent permission to access this specific tool
2. Retrieves scoped credentials: Fetches the user's OAuth token with appropriate permission boundaries
3. Executes the tool call: Runs the action (send email, query database, read calendar) on behalf of the user
4. Logs the action: Maintains immutable audit trail for compliance requirements
5. Returns results: Sends tool output back to LangChain agent without exposing credentials

This zero-token-exposure architecture means LLMs never see API keys, OAuth tokens, or database credentials. Credentials stay encrypted in Arcade's secure storage, retrieved only at execution time with validated user context.

For pharmaceutical deployments, Arcade's deployment flexibility addresses regulatory requirements that cloud-only solutions cannot meet. Teams can run Arcade in their own VPC or on-premises environments while maintaining the same tool catalog and authorization patterns.

Why MCP Compatibility Matters for Enterprise Deployments

The Model Context Protocol (MCP) standardizes how AI agents access tools and data sources. Arcade's native MCP support means pharmaceutical teams can:

• Connect to any MCP server over HTTP transport
• Use tools from the broader MCP ecosystem
• Build custom MCP servers for proprietary lab systems
• Maintain compatibility as LangChain and other frameworks adopt MCP

This matters because pharmaceutical AI infrastructure is fragmented and domain-specific. A clinical development agent might need access to:

• Commercial SaaS platforms (Salesforce, Veeva, Medidata)
• Internal databases and LIMS systems
• Cloud storage (Google Drive, SharePoint)
• Communication tools (Gmail, Slack, Microsoft Teams)
• Regulatory submission portals

MCP compatibility means these tools work together through a common protocol rather than requiring custom integration code for each system. Pharmaceutical teams can add new tools to their agent workflows without rebuilding authorization infrastructure.

Use Case 1: Automating Literature Review and Drug Discovery Research

Drug discovery research drowns in data. PubMed indexes over 35 million biomedical citations. Chemical compound databases contain billions of molecular structures. Clinical trial registries track hundreds of thousands of studies. Patents, regulatory documents, and internal research notes add layers of scientific knowledge trapped in unstructured formats.

Manual literature review consumes weeks of researcher time per project. Scientists search databases, download PDFs, extract relevant findings, track citations, and synthesize insights across dozens of sources. This workflow fragments researcher attention and misses connections between disparate studies that AI agents can surface instantly.

Drug discovery and lead generation represents 36% of the AI in the biotech market specifically because these research workflows benefit immediately from automation. AI agents that can search, filter, summarize, and synthesize biomedical literature at scale deliver measurable time savings while improving research quality through comprehensive coverage.

The agent workflow operates like this:

• Query decomposition: Breaking complex research questions into focused search strategies
• Multi-source retrieval: Parallel searches across PubMed, clinical trial registries, compound databases, patents, and proprietary research archives
• Relevance filtering: Applying domain-specific criteria to identify high-value sources
• Citation extraction: Building knowledge graphs linking compounds, targets, diseases, and outcomes
• Synthesis and summarization: Generating comprehensive literature reviews with source attribution

Building this without Arcade means implementing authorization separately for each data source. PubMed requires API keys. Proprietary compound databases need OAuth flows. Google Drive access for internal research notes requires Google authentication. Each integration consumes development time and creates maintenance burden.

Production literature review agents combine retrieval-augmented generation (RAG) with multi-step reasoning to deliver researcher-quality analysis. The agent doesn't just keyword search , it understands research context, evaluates study quality, identifies conflicting results, and synthesizes findings across methodologies.

For a query like "novel EGFR inhibitors for non-small cell lung cancer with reduced cardiotoxicity," the agent:

1. Expands the query: Identifies synonyms, related targets, mechanism variations
2. Searches systematically: Queries PubMed, ClinicalTrials.gov, compound databases, patent filings
3. Applies quality filters: Prioritizes peer-reviewed studies, clinical trial data, validated assays
4. Extracts structured data: Compound structures, target interactions, ADMET predictions, clinical outcomes
5. Identifies patterns: Common mechanisms, toxicity pathways, structure-activity relationships
6. Generates summaries: Comprehensive reports with citations and confidence assessments

The agent workflow respects information hierarchies , clinical trial results outweigh in-silico predictions, systematic reviews outweigh single case studies, direct measurements outweigh inferred relationships. This domain-specific reasoning requires sophisticated LangChain orchestration paired with Arcade's secure access to diverse data sources.

Business Impact Across Teams: AI/ML teams iterate faster with pre-built authorization infrastructure instead of spending months on OAuth flows for each data source. Security teams gain auditable access patterns showing exactly which researchers accessed which proprietary compound databases, when, and why. Research leadership redirects scientist capacity from manual literature searches to high-value hypothesis generation and experimental design.

Connecting Agents to Proprietary Compound Databases with OAuth Security

Many pharmaceutical companies maintain internal compound databases containing proprietary molecular structures, screening results, ADMET predictions, and intellectual property considerations. These databases represent significant competitive assets that require strict access controls.

Traditional approaches grant system-level database access to applications, creating security risks and compliance headaches. When an AI agent has database admin credentials, any user can potentially access any compound , violating need-to-know principles and creating audit trail gaps.

Arcade's authorization model enforces user-specific permissions. When Dr. Smith queries the compound database through an AI agent, the agent inherits Dr. Smith's database permissions , accessing only compounds and projects Dr. Smith is authorized to view. When Dr. Jones uses the same agent, it operates within Dr. Jones's permission boundaries.

This delegated authorization pattern requires:

• Integration with identity providers (LDAP, Active Directory, Okta)
• OAuth flows for proprietary systems using custom auth providers
• Scoped token issuance matching user database permissions
• Just-in-time credential retrieval at execution time

Building this infrastructure for a single database might take a development team 2-3 months. Scaling it across the dozens of systems pharmaceutical agents need to access creates compounding complexity that Arcade's platform abstracts away.

Use Case 2: Clinical Trial Coordination and Patient Communication Workflows

Clinical trials represent the most expensive and time-consuming phase of drug development. The administrative burden consumes resources that could accelerate research.

A typical multi-site Phase III trial involves:

• 50-200 clinical investigators across 20-100 sites
• Hundreds to thousands of patient participants
• Continuous monitoring for adverse events and protocol deviations
• Regulatory submission documentation and updates
• Site performance tracking and resource allocation
• Patient recruitment, screening, consent, and follow-up communications

Coordinating this complexity through email, spreadsheets, and manual data entry creates bottlenecks, errors, and delays. AI agents can automate significant portions of these workflows while maintaining the human oversight necessary for patient safety and regulatory compliance.

The clinical trial coordination agent operates across:

• Email and calendar systems: Scheduling visits, sending reminders, coordinating investigator meetings
• Clinical trial management platforms: Updating milestones, tracking enrollment, flagging protocol deviations
• Patient communication: Automated appointment confirmations, educational content delivery, adherence check-ins
• Regulatory documentation: Generating progress reports, compiling safety data, maintaining audit trails

Without Arcade, building authorization for this multi-system workflow requires custom OAuth implementations for Gmail, Google Calendar, Salesforce/Veeva, communication platforms, and proprietary trial management systems , multiplied across potentially hundreds of users at multiple organizations.

Building a Gmail + Calendar Agent for Patient Appointment Scheduling

Patient appointment scheduling exemplifies clinical trial coordination complexity. Each appointment requires:

• Checking patient availability and preferences
• Verifying investigator site capacity
• Confirming protocol visit windows and procedures
• Sending appointment confirmations and reminders
• Handling rescheduling requests and cancellations
• Documenting no-shows and protocol deviations

An AI agent can automate this workflow while maintaining appropriate human oversight. The agent reads patient preferences from trial management systems, checks investigator calendars for availability, proposes appointment times, sends calendar invites, and sets up automated reminders.

Building an AI agent for Gmail demonstrates this pattern. The agent needs delegated access to:

• Gmail API: Reading patient responses, sending appointment confirmations
• Google Calendar API: Checking availability, creating calendar events, sending invites
• Trial management system: Recording scheduled visits, tracking protocol compliance

Arcade handles the OAuth flows for Gmail and Calendar while providing a framework for integrating proprietary trial systems. The agent operates with each coordinator's credentials, respecting their existing access permissions rather than requiring new system-level accounts.

The business impact is measurable: clinical coordinators report spending 25% of time on appointment scheduling. Automation through AI agents redirects this capacity toward patient support and protocol adherence , activities that directly impact trial outcomes.

Slack Agents for Real-Time Trial Team Coordination

Multi-site clinical trials require constant communication among sponsors, CROs, site coordinators, investigators, and monitoring teams. Email becomes overwhelming. Important updates get buried. Urgent questions wait hours for responses.

Real-time communication platforms like Slack enable faster coordination, but create new problems: information fragmentation, notification overload, and difficulty tracking decisions across dozens of channels.

AI-powered Slack agents solve this by monitoring conversations, surfacing relevant information, and taking autonomous actions when appropriate. A clinical trial Slack agent might:

• Answer protocol questions: Retrieving information from protocol documents when investigators ask about procedures
• Surface relevant updates: Notifying appropriate team members when adverse events are reported or enrollment milestones are reached
• Coordinate across tools: Creating calendar events when meetings are discussed, updating trial management systems when decisions are made, sending email summaries to stakeholders not in Slack
• Maintain audit trails: Logging decisions and actions for regulatory compliance

The Archer Slack agent demonstrates this pattern with out-of-the-box integrations for Gmail, Google Calendar, and GitHub, plus customization for trial-specific systems. The agent operates within Slack's permission model while accessing external tools through Arcade's authorization layer.

Business Impact Across Teams: AI/ML teams focus on improving agent intelligence for protocol adherence rather than building custom integrations across trial management platforms, email, and calendars. Security teams enforce delegated permissions ensuring coordinators' agents can only access the specific trials and patient cohorts they're authorized to manage. Operations teams reduce clinical coordinator administrative burden by 25%, accelerating enrollment timelines and improving site performance.

Use Case 3: Supply Chain Management and Lab Inventory Procurement

Pharmaceutical research labs consume vast quantities of reagents, consumables, and specialized equipment. Running out of critical supplies halts experiments. Over-ordering ties up capital and risks reagent expiration. Manual procurement processes create delays and errors.

Lab managers typically handle procurement through:

• Periodic inventory reviews and reorder point calculations
• Vendor comparison for pricing and availability
• Purchase requisition submission and approval workflows
• Order placement and tracking
• Budget monitoring and spend reporting

AI agents can automate these workflows while maintaining necessary financial controls and approval gates. The procurement agent:

• Monitors inventory levels: Tracking usage patterns and predicting reorder points
• Compares vendor options: Searching Amazon, Walmart, and specialized scientific suppliers for pricing and availability
• Generates purchase requests: Creating requisitions with appropriate budget codes and approval routing
• Completes purchases: Executing approved orders with appropriate spend controls
• Maintains audit trails: Documenting every procurement decision for budget tracking and compliance

The supply chain use case demonstrates why agentic commerce capabilities matter for pharmaceutical AI agents. The agent needs to actually complete purchases, not just provide recommendations , requiring secure payment authorization with appropriate controls.

How Agentic Commerce Enables Compliant, Auditable Purchases

Traditional AI agents can search products and compare prices, but completing purchases requires payment authorization that most implementations can't safely provide. Giving an AI agent unrestricted credit card access creates obvious risks. Manual approval for every purchase eliminates automation benefits.

Arcade's agentic commerce suite solves this through just-in-time payment authorization with granular controls:

• Single-use virtual cards: Generated for specific transactions with exact amount limits
• Merchant restrictions: Cards locked to approved vendors only
• Transaction-specific limits: Amount, merchant, and time window constraints
• User approval workflows: Required authorization for purchases above defined thresholds
• Complete audit trails: Every transaction logged with full context and approval chain
• No persistent payment storage: Virtual cards self-destruct after purchase completion

For pharmaceutical procurement, this enables autonomous ordering within defined parameters while maintaining financial controls. A lab manager might configure:

• Automatic reordering for reagents under $500 from approved vendors
• Approval required for purchases over $500
• Blocked categories (no equipment purchases without explicit approval)
• Monthly spend limits per research group

The agent can browse, compare, add to cart, and complete checkout on Amazon and Walmart , with production-ready toolkits that replace brittle browser automation. Virtual cards are issued via Lithic's API with transaction-specific limits validated by independent auditors.

Setting Spend Limits and Merchant Restrictions for Lab Procurement

Pharmaceutical procurement requires balancing automation efficiency with financial controls. Different research groups have different budgets. Some purchases require additional approvals. Emergency orders might need expedited processing.

Arcade's agentic commerce framework enables configurable controls at multiple levels:

Budget-level controls:

• Monthly or quarterly spending caps per research group
• Automatic alerts as budgets approach limits
• Tiered approval workflows based on purchase amounts

Merchant restrictions:

• Approved vendor lists for specific categories
• Blocked merchants or product types
• Preferred vendor pricing agreements

Transaction controls:

• Purchase amount thresholds triggering human approval
• Time windows for automated purchases (business hours only)
• Geographic restrictions for fraud prevention

Observability and audit:

• Real-time monitoring dashboards for lab managers
• Complete transaction histories with line-item detail
• Integration with accounting systems for budget tracking

Business Impact Across Teams: AI/ML teams deploy agentic commerce capabilities without implementing payment authorization infrastructure from scratch. Security teams maintain complete audit trails for every autonomous purchase with transaction-specific limits and merchant restrictions that satisfy financial controls. Lab managers eliminate procurement bottlenecks while finance teams gain real-time budget visibility and automated spend tracking across research groups.

Getting Started: LangChain Documentation and Arcade Setup for Pharma Teams

Implementing AI agents in pharmaceutical environments requires careful planning around security, compliance, and workflow integration. The technical implementation, however, can start small and scale progressively.

Pharmaceutical teams should begin with a single, well-defined use case rather than attempting comprehensive automation immediately. This approach:

• Validates technical infrastructure before broad deployment
• Demonstrates business value to secure stakeholder buy-in
• Identifies integration challenges in a contained scope
• Builds team expertise with manageable complexity
• Establishes compliance patterns for future expansion

A typical starting point might be literature review automation for a single research team, patient appointment scheduling for a specific trial, or lab supply reordering for common reagents. These use cases deliver measurable value while limiting scope and risk.

Installing LangChain and Arcade in a Python Environment

The technical foundation requires setting up LangChain for agent orchestration and Arcade for secure tool access. Teams typically work in Python environments using virtual environments or containerized deployments.

The high-level setup process:

1. Create isolated environment: Virtual environment or Docker container for dependency management
2. Install LangChain: Core framework plus extensions for specific use cases (document loaders, vector stores, retrievers)
3. Install Arcade SDK: Client library for tool execution and authorization
4. Configure environment variables: API keys for LangChain LLM providers and Arcade platform access
5. Initialize tool connectors: Set up Arcade connections to Gmail, Slack, databases, or custom systems

The Arcade quickstart guide walks through basic setup, while LangChain integration documentation details how to use Arcade tools within LangChain agent workflows.

For pharmaceutical environments, deployment typically happens in stages:

• Development: Local or cloud-based testing with synthetic data
• Staging: Internal pilot with limited user access and real but non-critical workflows
• Production: Gradual rollout with monitoring and human oversight

Teams with strict compliance requirements often deploy Arcade's self-hosted option within their VPC or on-premises infrastructure to maintain data residency and control.

Configuring Your First Authenticated Tool for a Pharma Use Case

The critical setup step is configuring tool authorization to enable AI agents to act on behalf of users. A literature review agent, for example, needs secure access to PubMed, internal document repositories, and potentially proprietary compound databases.

For OAuth-enabled services like Gmail or Google Drive, Arcade provides pre-built auth providers that handle token management automatically. Teams configure:

1. OAuth application credentials: Registering your application with the service provider (Google, Microsoft, Salesforce)
2. Arcade auth provider setup: Connecting Arcade to your OAuth application
3. User authorization flow: How users grant the agent permission to access their accounts
4. Scope configuration: What specific permissions the agent requests

For proprietary internal systems, teams use Arcade's custom OAuth 2.0 provider to integrate with corporate identity systems. This requires:

• Implementing OAuth 2.0 authorization server (often already exists in enterprise IAM)
• Defining scopes matching database or API permissions
• Integrating with Arcade's authorization flow
• Testing delegated access with different user permission levels

The secure auth in production guide covers hardening authorization flows, implementing custom branding, and handling edge cases like token expiration and user access revocation.

Pharmaceutical teams typically start with one well-understood system (company Gmail or Calendar), validate the authorization pattern works correctly, then expand to additional tools incrementally.

Security, Compliance, and Data Privacy for Pharma AI Agents

Pharmaceutical AI agents handle sensitive research data, proprietary intellectual property, patient information, and trade secrets. Security failures create regulatory violations, 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 clinical trial coordination agent requires access to patient databases, investigator communications, regulatory submissions, and trial management systems. 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.

Tech companies use 2+ control methods 51% of the time compared to 39% for non-tech organizations, but pharmaceutical companies need enterprise-grade security regardless of technical maturity. The requirements include:

• Zero token exposure: LLMs never see API keys, OAuth tokens, or database credentials
• Delegated authorization: Agents inherit user-specific permissions, not system admin access
• Just-in-time credential retrieval: Tokens accessed only at execution time, not stored in agent context
• Granular scope enforcement: Tools receive only the permissions necessary for specific actions
• Complete audit trails: Every agent action logged with user context, timestamp, and outcome
• User approval workflows: Sensitive operations require explicit human authorization before execution

Building these controls without a purpose-built platform means implementing OAuth flows, token lifecycle management, permission scoping, and audit logging for every integrated system , multiplied across dozens of tools and hundreds of users.

How Arcade Ensures Tokens Are Never Exposed to LLMs

The fundamental security problem in AI agent architectures is that LLMs need to call tools, but tools require credentials, and giving LLMs access to credentials creates unacceptable risks. An LLM with database credentials could leak them in generated text. An LLM with OAuth tokens could use them in ways users never authorized.

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

1. Agent requests tool execution: LangChain agent decides to call a tool but doesn't have credentials
2. Arcade validates authorization: Confirms the user has granted permission for this specific tool
3. Arcade retrieves scoped token: Fetches encrypted credential with appropriate permissions
4. Arcade executes action: Calls the tool API on behalf of the user
5. Arcade returns results: Sends tool output back to agent 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 authorization tokens required to execute actions.

This zero-token-exposure architecture is validated through SOC 2 Type 2 certification:

• 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

For pharmaceutical companies, this means AI agents can access sensitive systems with the same security guarantees as human users , delegated permissions, scope-limited access, and full audit trails.

Custom AI Agents Tools: Building Proprietary Integrations for High Res Biosolutions

Pharmaceutical and biotech companies operate fragmented, domain-specific enterprise systems: laboratory information management systems (LIMS), electronic lab notebooks (ELN), compound databases, assay platforms, manufacturing execution systems, and clinical data warehouses. These proprietary platforms contain the most valuable data for AI agents but lack standardized APIs or OAuth support.

Building AI agents that access only commercial SaaS platforms (Gmail, Slack, Salesforce) delivers limited value for pharmaceutical R&D. The high-impact use cases require secure access to internal systems where competitive advantages exist , novel compound data, proprietary screening results, custom assay protocols, and experimental findings.

Arcade's custom tool SDK enables pharmaceutical teams to wrap internal APIs as authenticated agent tools without rebuilding authorization infrastructure. The framework handles:

• OAuth integration with corporate identity providers
• Token lifecycle management and refresh logic
• Scoped permission enforcement
• Audit logging and compliance documentation
• Error handling and retry logic
• Tool evaluation and testing frameworks

Teams focus on defining what actions the tool exposes and how it interacts with internal APIs, while Arcade manages the authorization, security, and operations layers.

Using Arcade's SDK to Wrap Your Internal APIs as Agent Tools

The custom tool development process follows a standard pattern:

1. Define tool capabilities: What actions should the agent be able to perform? (query compounds, submit assays, retrieve results, update protocols)
2. Map to internal APIs: How do these actions translate to specific API calls in your LIMS or database?
3. Implement authorization: How should user permissions map to API access levels?
4. Build tool wrapper: Write the integration code using Arcade's SDK
5. Test and validate: Use Arcade's evaluation framework to verify correct behavior
6. Deploy and monitor: Push to production and track performance

For a proprietary compound database, the tool might expose:

• Search compounds by structure, properties, or activity
• Retrieve detailed compound records including synthesis protocols
• Query screening results from specific assays
• Update compound annotations with new findings

Each action inherits the user's database permissions. A medicinal chemist sees compounds in their research program. A computational chemist accesses screening data for their models. A manager reviews aggregated results across programs. The same agent tool operates differently depending on who's using it.

Most Custom Tools Take Less Than 30 Minutes to Build

Arcade's SDK design prioritizes developer velocity for teams that understand their internal systems but may not be authorization experts. The framework provides:

Pre-built authorization patterns:

• OAuth 2.0 flows for SSO integration
• API key management for systems with simpler auth
• Database credential handling for direct SQL access

Standard tool interfaces:

• Input parameter validation and typing
• Output schema definition and validation
• Error handling with retry logic
• Logging and observability hooks

Testing and evaluation:

• Unit test frameworks for tool logic
• Integration tests against staging systems
• Performance benchmarking tools
• LLM evaluation for tool selection and usage

A typical custom tool for querying an internal PostgreSQL database takes 15-30 minutes to implement: define the query parameters, write the SQL logic, configure database credentials, add basic error handling. The SDK handles token retrieval, connection management, audit logging, and deployment.

More complex integrations with proprietary LIMS or assay platforms might take a few hours to a few days depending on API complexity and documentation quality. But teams spend time on business logic and domain expertise, not rebuilding OAuth flows or token refresh handlers for the hundredth time.

The tool context documentation and error handling guide provide detailed technical guidance for production-quality tool development.

Real-World AI Agents Examples: Teams Already Using LangChain and Arcade in Life Sciences

While pharmaceutical-specific public case studies remain limited due to competitive sensitivity and regulatory caution, adjacent industries demonstrate validated patterns pharmaceutical teams can adapt.

Healthcare AI Agents: The Stride clinical AI agent implementation shows production architecture for healthcare workflows using LangGraph orchestration, confidence threshold-based human escalation, and read-only access to historical data with write capabilities for new actions. The agent handles patient inquiries, appointment scheduling, and clinical documentation , workflows analogous to clinical trial coordination.

Integration Partners Validation: Arcade's integration with companies like Snyk, Relevance AI, and Sybill demonstrates production tool-calling at scale, though specific pharmaceutical deployments remain confidential during early adoption phases.

LangChain Adoption Metrics: The State of AI Agents report surveying 1,300+ professionals shows that 51% already have agents in production, with life sciences companies among early adopters. The report reveals that tracing and observability tools are the top control mechanism organizations deploy , exactly what Arcade's platform provides through its authorization and audit capabilities.

Published Research Examples: Academic papers and industry presentations demonstrate multi-agent systems for pharmaceutical workflows:

• AI agent systems for drug discovery literature mining and hypothesis generation
• Multi-agent collaboration for omics data analysis and pathway interpretation
• Clinical trial optimization through predictive patient matching and site selection

Quantified Outcomes: While pharmaceutical-specific ROI data remains limited, adjacent implementations show measurable impact:

• Pfizer reported 20% throughput boost with AI tools in manufacturing
• Doubled scientist output in drug discovery workflows through automation
• Significant reductions in clinical study report generation time

The pattern across these examples is consistent: organizations succeed when they start with a focused use case, validate security and compliance requirements early, and scale incrementally based on demonstrated value. Pharmaceutical teams adopting AI agents follow this same playbook , pilot with literature review or simple coordination tasks, prove value and compliance, then expand to more complex workflows.

As regulatory frameworks mature and more pharmaceutical companies publish results, the evidence base for AI agent ROI will strengthen. Early adopters establishing production deployments now build competitive advantages before the market fully matures.

Frequently Asked Questions

Can I deploy LangChain and Arcade agents in a self-hosted environment for HIPAA compliance?

Yes. Arcade supports self-hosted deployment in your VPC or on-premises infrastructure with identical authorization patterns and tool integrations across cloud, VPC, or air-gapped environments. Pharmaceutical teams typically deploy within their VPC with encryption at rest and in transit, audit logging feeding corporate SIEM systems, and Business Associate Agreements with third-party providers. Agent code remains portable , develop and test in cloud environments, then deploy to compliant infrastructure without code changes.

How does Arcade.dev ensure tokens are never exposed to LLMs in drug discovery workflows?

Arcade enforces strict separation between agent reasoning (handled by LLMs) and tool execution (handled by Arcade's runtime) , when agents invoke tools, Arcade validates authorization, retrieves encrypted credentials from secure storage, executes actions, and returns only results, never credentials. This zero-token-exposure architecture is validated through SOC 2 Type 2 certification with independent auditor verification of just-in-time authorization, tool-level access controls, and complete audit trails.

What does it cost to run AI agents for clinical trial coordination using Arcade's Growth plan?

A typical single-site trial agent serving 10 investigators executing 4,000 tool calls monthly costs approximately $45/month on Arcade's Growth plan ($25 base plus $20 in overage at $0.01 per execution beyond the included 2,000). Multi-site trials scaling to 50 investigators might execute 15,000-20,000 monthly tool calls, totaling $150-180/month before volume discounts , dramatically lower than the $15,000-25,000 annually in clinical coordinator labor costs per site.

How long does it take to build a custom tool for a proprietary lab information management system (LIMS)?

Most custom tools using Arcade's SDK take 15 minutes to a few hours depending on API complexity , well-documented REST APIs with OAuth support yield basic query tools in 15-30 minutes, while complex LIMS integrations requiring multiple endpoints might take 4-8 hours. Arcade's custom tool SDK eliminates weeks of OAuth flow development, token management, and infrastructure work, letting teams focus on domain logic. Budget a day for your first tool as you learn patterns, then 1-2 hours for additional tools.

Which LangChain documentation should pharma developers start with when building their first agent?

Start with LangChain's agent fundamentals covering chain composition and tool selection, then move to RAG tutorials for document-based workflows, prioritizing how agents decompose complex tasks and manage multi-source retrieval across PubMed, compound databases, and clinical trial registries. Review Arcade's LangChain integration guide for authorization handling, then use pre-built examples like the Gmail agent tutorial and Google Calendar agent with LangChain as working code to adapt for pharmaceutical use cases.

What security controls should pharmaceutical teams implement when deploying AI agents to production?

Production pharmaceutical AI agents require layered controls including tracing and observability (the most common control organizations deploy), human-in-the-loop approval for write operations, and Arcade's delegated authorization ensuring agents inherit user-specific permissions rather than system-level access. Implement confidence thresholds for escalation, maintain complete audit trails with Arcade's compliance logging, deploy in VPC environments when handling sensitive data, and regularly evaluate performance using Arcade's evaluation suite. Start with minimum viable agent scope, prove security controls work correctly, then expand capabilities incrementally rather than granting broad permissions upfront.