Custom Pharma AI Agents & Agentic AI

Autonomous AI systems that execute complex pharmaceutical workflows end-to-end, with human oversight at every critical decision point.

Why Pharma Needs Purpose-Built AI Agents

The global AI in drug discovery market alone is projected to exceed $10 billion by 2028, and the broader pharmaceutical AI market is growing at a 25-30% CAGR. But most off-the-shelf AI tools are generic: they lack the domain knowledge, compliance infrastructure, and data integration depth that pharmaceutical workflows demand. A regulatory intelligence agent must understand ICH CTD structure. A pharmacovigilance agent must know MedDRA coding conventions. A clinical operations agent must respect GCP protocol requirements. IntuitionLabs builds agents with this domain specificity embedded at the architecture level, not bolted on as prompts. Our agents connect to the systems pharma teams actually use: Veeva Vault, SAP, Oracle, clinical trial databases, and regulatory submission platforms.

Pharma Domain Architecture

Domain knowledge at the architecture level, not just in prompts. Temporal-orchestrated with guaranteed execution, retry logic, and state persistence.

Multi-Agent Orchestration

Multi-agent orchestration for complex parallel tasks. RAG grounded in your SOPs, regulatory filings, and internal knowledge base.

Compliance & Audit Trails

Full audit trails for 21 CFR Part 11 and Annex 11. Validation-ready per ISPE GAMP 5 Second Edition for AI/ML systems.

Beyond Chatbots: AI That Acts

Agentic AI fills the gap where rule-based automation breaks down — workflows requiring judgment, unstructured text interpretation, or multi-step decision-making. IntuitionLabs builds custom pharma AI agents on Temporal durable workflow infrastructure with GxP-compliant guardrails and human-in-the-loop approval gates at every regulated decision point.

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AI agent architecture diagram for pharmaceutical workflows

Agentic AI Architecture for Pharmaceutical Workflows

An AI agent combines LLM reasoning, planning, tool use, memory, and execution control into a coherent loop for complex multi-step objectives. The foundational ReAct (Reasoning + Acting) framework — interleaving chain-of-thought reasoning with concrete tool-use actions — is the model for every pharmaceutical agent we build, dramatically improving both task accuracy and interpretability.

AI agent architecture for pharmaceutical workflows

The ReAct Loop: Reasoning and Acting in Tandem

Each agent cycle: receive an observation, produce a chain-of-thought reasoning trace, execute a tool, observe the result, and repeat until the objective is met or a human must intervene. In pharmacovigilance, the agent reads new FDA FAERS adverse event batches, assesses relevance to your portfolio, and drafts a signal assessment — every step logged and auditable.

ReAct loop diagram for pharmaceutical AI agents

Tool-Use Patterns and Function Calling

Every tool has a defined input/output contract, rate limits, authentication, and access-control policy — an agent cannot invoke a tool unless explicitly authorized. Common categories: database queries (Veeva Vault, SAP), document retrieval from vector databases, ClinicalTrials.gov / Drugs@FDA APIs, statistical calculators, and notification tools.

Advanced Agent Architecture Patterns

Beyond the ReAct loop, production pharmaceutical AI agents rely on four additional architectural patterns that determine reliability, transparency, scalability, and cost. Understanding these patterns is essential for building agents that perform consistently in regulated environments.

Chain-of-Thought Reasoning and Transparency

Chain-of-thought (CoT) prompting produces interpretable reasoning traces before every action — improving multi-step accuracy and providing the auditable decision trail regulators expect. The trace shows exactly what was analyzed, compared, and concluded, making agent decisions transparent and defensible.

AI compliance and auditability

Multi-Agent Orchestration

Using LangGraph and Temporal child workflows, we decompose complex tasks into specialized sub-agents communicating through defined interfaces. An orchestrator manages information flow, handles failures and retries, and ensures each agent operates within its authorized scope — with exactly-once execution semantics.

Temporal orchestration deep dive

Memory Systems: Short-Term and Long-Term

Short-term memory holds context within a single run — retrieved documents, intermediate calculations, and prior steps. Long-term memory persists across runs via vector databases, letting agents learn which sources proved most useful and which regulatory topics are trending, improving output quality over time.

RAG and memory in pharma AI

Planning vs. Execution Separation

A high-capability model decomposes the task into a structured plan — steps, dependencies, and fallbacks — reviewable by a human before execution. Execution may use smaller, cheaper models per step and can be parallelized. If a step fails, the planner revises without restarting. Implemented on Temporal with full durability.

Risk-based AI validation

Hallucination Mitigation and Source Grounding

We ground agent responses in verified source documents via RAG rather than relying on parametric knowledge alone. Every factual claim includes a citation to its source document or API response. Confidence scoring flags low-confidence outputs for human review; a separate verifier agent checks safety-critical outputs before release.

GxP compliance and hallucination controls

Model-Agnostic Architecture

Agents select the right LLM per task: frontier models (Claude Opus, Gemini Pro) for complex regulatory analysis, smaller models for high-throughput classification. For strict data-residency, we deploy open-weight Llama or Mistral on-premise — no data leaves your network.

LLM selection and pricing

AI Agent Use Cases Across the Pharmaceutical Value Chain

Regulatory Intelligence

Agents that continuously monitor FDA guidance documents, EMA reflection papers, ICH guideline updates, and Health Authority meeting minutes, analyzing impacts on your product portfolio and regulatory strategy.

Pharmacovigilance Signal Detection

Agents that ingest adverse event data from FDA FAERS, EudraVigilance, and internal safety databases to identify, assess, and report emerging safety signals with full traceability.

Clinical Trial Site Selection

Agents that analyze ClinicalTrials.gov historical enrollment data, investigator publication records, site infrastructure capabilities, and patient population demographics to recommend optimal trial sites for each indication.

Medical Writing Assistance

Agents that draft clinical study reports, regulatory submission sections, and scientific publications from structured clinical data, following ICH M4 CTD formatting requirements with full source traceability.

Supply Chain Disruption Prediction

Agents that monitor global supply indicators, API supplier financials, logistics network data, and geopolitical signals to predict and mitigate supply chain disruptions before they impact manufacturing schedules.

Patent Landscape Mapping

Agents that continuously scan patent filings, monitor patent expiration timelines, analyze FDA Orange Book and Purple Book listings, and map competitive IP positions across therapeutic areas.

Formulary Access Strategy

Agents that analyze payer formulary structures, step therapy requirements, prior authorization criteria, and competitive pricing to optimize market access strategies for new product launches.

REMS Program Management

Agents that automate Risk Evaluation and Mitigation Strategy compliance tracking, patient enrollment verification, prescriber certification monitoring, and periodic REMS assessment report generation.

MLR Review Acceleration

Agents that pre-screen promotional materials for compliance with Medical-Legal-Regulatory requirements, flagging potential issues, checking claims against approved labeling, and verifying fair-balance statements before human review.

Regulatory Intelligence Agent

Running daily, the agent retrieves and classifies publications from FDA, EMA, PMDA, and WHO, then generates impact assessments for your product portfolio — identifying labeling updates, submission amendments, or strategy revisions. Findings are routed to the RA team for review, with escalation to senior leadership for high-impact changes.

Clinical Trial Site Selection Agent

Given a protocol's indication and enrollment targets, the agent queries ClinicalTrials.gov for site enrollment history, cross-references PubMed for investigator expertise, and estimates patient population from epidemiological data. Output is a ranked site scorecard — designed for clinical ops review before final selection, following ICH E8(R1) principles.

Supply Chain Disruption Prediction Agent

The agent monitors supplier financials, FDA drug shortage notices, warning letters, logistics anomalies, and geopolitical signals. When a risk pattern is detected, it maps impact to your bill of materials and recommends a mitigation plan — delivered as a risk bulletin routed to supply chain and quality teams for action.

Supply chain disruption prediction and risk monitoring agent

Patent Landscape Mapping Agent

The agent continuously scans global patent databases, parsing claims by compound structure, method-of-use, and formulation. It maps filings to the competitive landscape, tracks Orange Book and Purple Book exclusivity windows, and flags upcoming patent cliffs — delivering landscape reports with confidence scores for Paragraph IV and licensing strategy review.

More Agent Use Cases in Practice

Beyond the walkthroughs above, IntuitionLabs builds agents across additional high-value pharmaceutical workflows — each following the same architecture: GxP-compliant, human-in-the-loop, and integrated with your existing systems.

Formulary Access Strategy Agent

Analyzes payer coverage decisions, step therapy requirements, and prior authorization criteria across major PBMs. Maps tier placements and PA requirements for your products and competitors, simulates pricing scenarios for new launches, and monitors payer policy changes in near real-time.

Formulary strategy deep dive

REMS Program Management Agent

Automates the operational burdens of REMS compliance: patient enrollment tracking, prescriber certification verification, and periodic assessment report compilation in FDA-specified formats — all routed through human review before submission.

REMS compliance approach

Medical Writing Assistance Agent

Ingests structured clinical data and generates first drafts of standardized sections: demographics tables and efficacy narratives following ICH E3, and integrated summaries in ICH M4 CTD format. Every statement includes a traceable source reference. Medical writers retain full authorial control.

Medical writing automation

AI Model Selection for Pharmaceutical Applications

Choosing the right LLM for each agent task is one of the most consequential architectural decisions in pharmaceutical AI. We take a model-agnostic approach, selecting the optimal model for each specific task within an agent rather than committing to a single provider across all workflows.

Model Size and Task Matching

Large frontier models (Claude Opus, Gemini Pro, GPT-4o) handle complex reasoning. Mid-size models balance cost and capability. Smaller fine-tuned models suit high-throughput tasks like MedDRA coding — reducing LLM costs by 60–80% versus using a frontier model for every step.

Open-Weight vs. Proprietary Models

Proprietary APIs (Anthropic, Google, OpenAI) deliver top performance; open-weight models (Llama, Mistral, Qwen) can be self-hosted for strict data-residency requirements. We routinely deploy hybrid architectures separating sensitive from non-sensitive workloads.

On-Premise vs. Cloud Deployment

On-premise requires GPU infrastructure (A100/H100) but suits high-volume or air-gapped environments. Cloud APIs offer elastic scaling and faster time-to-value. We evaluate total cost of ownership — infrastructure, operations, and update cadence — to recommend the right deployment model per use case.

Data Architecture for Pharmaceutical AI Agents

The quality and accessibility of data is the single largest determinant of AI agent effectiveness. Pharmaceutical organizations possess vast amounts of valuable data, but it is typically fragmented across dozens of siloed systems, stored in incompatible formats, and governed by complex access control policies. Building effective AI agents requires a deliberate data architecture that makes the right data available to the right agent at the right time, with appropriate security and audit controls.

RAG and Vector Retrieval

RAG performance depends on embedding model quality and chunking strategy. We use domain-optimized embeddings with chunking tailored by document type — section-level for regulatory docs, paragraph-level for SOPs. Hybrid retrieval (dense vector + BM25 sparse) consistently outperforms either approach alone on pharma document benchmarks.

RAG in pharma documents

Structured Data Access Patterns

Pharmaceutical agents query structured data across relational databases and APIs — CDISC clinical data, manufacturing batch records, and CRM systems. We build tool interfaces with well-defined schemas and parameterized queries, preventing arbitrary SQL execution and ensuring agents can only access authorized data.

RAG for drug discovery data

Data Pipelines and Unstructured Document Processing

We connect agents to data lakes via ETL pipelines on configurable schedules, with CDC patterns for real-time use cases. For unstructured data — scanned lab notebooks, handwritten batch records, legacy regulatory PDFs — we deploy OCR and layout analysis pipelines. LLM vision handles complex multi-column filings that traditional OCR cannot process reliably.

Document AI benchmarks

Security and Access Control for Pharmaceutical AI Agents

AI agents accessing sensitive pharmaceutical data require enterprise-grade security aligned with the NIST AI Risk Management Framework and ISO/IEC 42001. Every layer — identity, secrets, and network — is enforced from day one.

Authentication and Authorization

Every agent operates under a defined identity tied to your existing IdP (Active Directory, Okta). RBAC scopes are evaluated at every tool invocation — not just startup — preventing privilege escalation. All authorization decisions are logged for 21 CFR Part 11 audit readiness.

Secret Management

API keys, database credentials, and service tokens are stored in HashiCorp Vault or AWS Secrets Manager — never in env vars or agent prompts. Secrets are injected at runtime with automatic rotation. The agent runtime actively prevents secrets from appearing in LLM reasoning traces or audit logs.

Network Isolation and Data Residency

Agents run in network-isolated environments with explicit egress allowlists. For EU GDPR or sovereign-cloud requirements, agents and data stores are pinned to the required region. The most sensitive deployments use air-gapped environments with self-hosted LLMs and zero external connectivity. See our GxP compliance guardrails.

Monitoring and Observability for Production AI Agents

Operating AI agents in pharmaceutical production requires observability beyond traditional application monitoring — covering what agents decided, why, and how well they performed over time.

Operational Metrics

Tracks completion time, latency per step, error rates, LLM token usage, and queue depth. Dashboards export to Datadog, Grafana, or CloudWatch, with alerting thresholds configurable per workflow SLA.

Quality and Accuracy Metrics

Automated evaluation pipelines score output accuracy against curated ground-truth datasets. Precision, recall, and F1 metrics are tracked per task type, with drift detection triggering human review when scores fall below thresholds.

Audit Trail and Compliance Reporting

Temporal workflow event history provides an immutable, timestamped record of every agent action, tool invocation, and human approval. This directly satisfies 21 CFR Part 11 and EU Annex 11 audit trail requirements.

Human-in-the-Loop Patterns for Regulated Pharma

In pharmaceutical operations, full autonomy is rarely appropriate. Regulatory requirements, patient safety considerations, and the consequences of errors demand that humans remain in control of critical decisions while AI agents handle the data-intensive preparatory work. We implement a spectrum of human-in-the-loop patterns calibrated to the risk profile of each workflow step, following ICH Q9 risk-based decision-making principles. We define five levels of agent autonomy: Level 1 (Full Human Control), Level 2 (Approval Gates), Level 3 (Exception-Based Review), Level 4 (Audit-Based Oversight), and Level 5 (Full Autonomy) — reserved for non-GxP tasks only.

Approval Gates and Escalation

Temporal signals pause execution until an approver reviews the output. Escalation routes to backup reviewers after a configurable timeout.

Confidence Thresholds and Routing

Routes agent outputs based on confidence score. High-confidence outputs proceed expedited; low-confidence route to full review with reasoning trace.

Feedback Loops for Continuous Improvement

Reviewer approvals, rejections, and edits are captured as training signal to update prompts, fine-tune classifiers, and improve retrieval parameters.

Compliance, Validation, and Regulatory Frameworks

Deploying AI agents in pharmaceutical environments requires navigating a complex and rapidly evolving regulatory landscape. Multiple frameworks at the international, regional, and national levels govern how AI can be used in pharmaceutical operations, and our agent architectures are designed to comply with all relevant requirements from the outset.

FDA AI/ML Guidance and GMLP

The Good Machine Learning Practice (GMLP) guiding principles, developed jointly by the FDA with Health Canada and UK MHRA, establish ten foundational principles for AI/ML in healthcare. The FDA AI/ML in Drug Development discussion paper outlines how the agency views AI use across the drug lifecycle. Our agents align with these principles: using representative, well-curated data; implementing continuous performance monitoring; maintaining transparent decision-making through logged reasoning traces; and supporting human oversight at appropriate decision points. For agents that generate regulatory submissions content, we ensure traceability satisfying eCTD submission requirements.

EU AI Act Risk Classification

The EU AI Act establishes a risk-based classification system that directly impacts pharmaceutical AI agents. AI systems used as safety components of products regulated under EU pharmaceutical legislation are classified as high-risk, requiring conformity assessments, technical documentation, quality management systems, logging, human oversight, and accuracy requirements. Our agent architectures include the technical documentation, logging, and quality management infrastructure required for EU AI Act compliance from the design phase.

ISPE GAMP 5 Second Edition

The ISPE GAMP 5 Second Edition provides the pharmaceutical industry standard framework for validating computerized systems, including AI/ML components. Our validation approach includes risk assessment that classifies each agent component by GxP impact, comprehensive validation protocols including input/output verification, boundary testing, robustness testing with adversarial inputs, and performance testing against evaluation datasets. Ongoing monitoring ensures agents continue to perform within validated parameters.

ICH Guidelines for AI Agent Use

Several ICH guidelines are directly relevant: ICH Q8 (Pharmaceutical Development) principles of Quality by Design inform agent output design. ICH Q9 (Quality Risk Management) provides the risk assessment framework. ICH Q10 (Pharmaceutical Quality System) guides our feedback loop and drift detection approach. ICH Q12 (Lifecycle Management) supports post-approval change management. ICH E6(R3) (Good Clinical Practice) addresses AI in clinical trials. ICH E9(R1) (Estimands) guides statistical analysis in clinical trial contexts.

NIST AI RMF and ISO/IEC 42001

The NIST AI Risk Management Framework (AI RMF 1.0) provides a structured approach to identifying, assessing, and managing AI risks across four functions: Govern, Map, Measure, and Manage. ISO/IEC 42001 specifies requirements for an AI management system, providing the organizational governance framework. The OECD AI Principles establish high-level principles for trustworthy AI including transparency, accountability, and human oversight that inform our agent design philosophy.

EMA Perspective on AI

The EMA reflection paper on AI in the lifecycle of medicines outlines the European regulatory perspective on AI use across drug development, manufacturing, and pharmacovigilance. The EMA emphasizes human oversight, data quality, and transparency. Our agents comply with the EMA expectation that AI systems must be explainable: every agent decision includes a reasoning trace and source citations that enable regulatory reviewers to understand and challenge the basis for any AI-generated analysis.

From Concept to Production Agent in Weeks

Weeks 1–2: domain discovery — data landscape, regulatory requirements, and success criteria. Weeks 3–4: working prototype on representative data. Weeks 5–8: refinement, production integration, and security hardening. Weeks 9–12: validation documentation, user training, and go-live. Each phase follows risk-based validation principles aligned with ICH Q9.

Integration with Your Enterprise Ecosystem

We build integrations with the platforms pharma teams rely on: Veeva Vault and CRM, SAP, Oracle Life Sciences, Salesforce Health Cloud, and clinical data management systems. Our integration layer handles auth, rate limiting, error recovery, and format translation. Organizations using MCP can expose enterprise sources as MCP servers consumed by any compatible agent.

Enterprise integration architecture connecting AI agents with pharmaceutical systems

Scaling from Single Agent to Multi-Agent Systems

Most organizations start with a single focused agent — regulatory intelligence or adverse event triage — then expand as confidence grows. Eventually agents collaborate: a regulatory agent detects a guideline change, triggers a labeling review, which triggers promotional material review. This evolution from isolated to interconnected agentic systems happens step by step, each new agent building on established infrastructure and governance.

Scaling from single agent to interconnected multi-agent pharmaceutical systems

Frequently Asked Questions About Pharma AI Agents

An AI agent is an autonomous software system that perceives its environment, reasons about goals, selects tools and data sources, executes multi-step workflows, and iterates until a task is complete. Unlike a chatbot, which responds to a single prompt with a single generation, an agent can call APIs, query databases, read regulatory documents, run calculations, draft outputs, critique its own work, and loop until quality thresholds are met. In pharma, this means an agent can ingest a new FDA safety alert, cross-reference it against your product labels stored in Veeva Vault, identify affected SKUs, draft a field safety notice, route it through MLR review, and track the approval status, all autonomously with human checkpoints at critical decision points.

Every agent we build produces a complete, immutable audit trail that satisfies 21 CFR Part 11 electronic-record requirements: timestamped logs of every LLM call, tool invocation, data access, and human approval decision. Agent state is persisted in Temporal workflows, providing durable execution history that survives infrastructure failures. We implement role-based access control so agents can only access data their operator is authorized to see. All agent outputs that feed into regulated processes pass through human-in-the-loop approval gates before being committed. Our validation approach follows ISPE GAMP 5 Second Edition guidance for AI/ML systems, with risk-based testing proportional to the GxP impact of each agent action.

We are model-agnostic and select the right model for each task based on accuracy, latency, cost, and data-residency requirements. For reasoning-heavy tasks like regulatory analysis we typically use large frontier models such as Claude Sonnet or Gemini Pro. For high-throughput classification or extraction tasks we use smaller, faster models like Gemini Flash. For organizations with strict data-residency requirements, we can deploy open-weight models such as Llama, Mistral, or Qwen on-premise or in a private cloud VPC, ensuring that no patient data or proprietary information leaves your network boundary. We routinely benchmark models against domain-specific evaluation datasets before selecting a production model for any agent.

A focused, single-workflow agent such as a regulatory intelligence monitor or a literature screening agent can be designed, built, validated, and deployed in eight to twelve weeks. More complex multi-agent systems involving several integrated workflows, multiple data sources, and extensive human-in-the-loop controls typically require twelve to twenty weeks. We follow an iterative delivery model: the first working agent is demonstrated within the first two to three weeks, then refined through successive sprints with domain-expert feedback. Validation documentation, including risk assessments, test protocols, and traceability matrices, is produced in parallel with development so it does not add a separate phase at the end.

Our agents integrate with virtually any structured or unstructured data source relevant to pharmaceutical operations. Common integrations include Veeva Vault and Veeva CRM, Salesforce Health Cloud, SAP S/4HANA, Oracle Life Sciences, clinical trial management systems, electronic lab notebooks, LIMS platforms, and safety databases. Agents also access public regulatory databases such as FDA FAERS, Drugs@FDA, ClinicalTrials.gov, EudraVigilance, the FDA Orange Book, and MedDRA. For unstructured knowledge retrieval, we build vector databases over your internal document corpus using retrieval-augmented generation so agents can answer questions grounded in your specific SOPs, protocols, and regulatory filings.

Hallucination mitigation is a first-class design concern in every agent we build. We use retrieval-augmented generation to ground agent responses in verified source documents rather than relying solely on parametric model knowledge. Every factual claim in agent output includes a citation linking back to the source document, database record, or API response that supports it. We implement confidence scoring so agents can flag low-confidence outputs for mandatory human review. Chain-of-thought reasoning traces are logged and auditable, allowing reviewers to inspect the reasoning path that led to any conclusion. For safety-critical workflows, we deploy a separate verifier agent that independently checks outputs against source data before they are released.

AI agent operating costs are driven primarily by LLM inference costs, which depend on the model selected, the number of tokens processed per run, and the volume of agent executions. A typical regulatory intelligence agent processing fifty documents per day might cost between fifteen and forty dollars per day in LLM inference, depending on model choice. We optimize costs through intelligent model routing, sending simple extraction tasks to small fast models and reserving large models for complex reasoning. Caching, prompt optimization, and batching further reduce per-run costs. Infrastructure costs for Temporal orchestration, vector databases, and monitoring are modest relative to LLM inference and scale predictably with usage.

Yes. IntuitionLabs is a Veeva X-Pages partner with deep expertise across the Veeva platform. Our agents integrate with Veeva Vault (QMS, RIM, PromoMats, MedComms, Clinical), Veeva CRM, and Veeva Compass via their respective APIs. Common integration patterns include agents that monitor Vault document lifecycle events and trigger downstream workflows, agents that enrich CRM records with external intelligence, and agents that automate content review workflows in PromoMats. All Veeva integrations respect the platform security model, using service accounts with least-privilege access and logging all API interactions for audit purposes.

Every production agent ships with a comprehensive observability stack. We track operational metrics including latency per step, end-to-end completion time, error rates, retry counts, and cost per run. Quality metrics are tracked through automated evaluation pipelines that score agent outputs against ground-truth datasets on a scheduled basis, detecting accuracy drift before it impacts business outcomes. We set up alerting thresholds so your team is notified when any metric deviates from its baseline. Temporal workflow dashboards provide real-time visibility into agent execution state, pending human approvals, and failure recovery. Monthly performance reports summarize trends and recommend model updates or prompt refinements when drift is detected.

Our agent architecture implements defense-in-depth security. All data in transit is encrypted with TLS 1.3 and data at rest is encrypted with AES-256. Agents run in isolated compute environments with no shared tenancy. Secrets such as API keys and database credentials are managed through dedicated secret stores with automatic rotation, never hardcoded or stored in agent memory. Network policies restrict agent egress to explicitly allowlisted endpoints. Agent memory and state stored in Temporal is encrypted and access-controlled. For cloud deployments, we support deployment within your own VPC with private endpoints, ensuring data never traverses the public internet. All access is logged and auditable, with integration into your existing SIEM for security monitoring.

Build AI Agents That Transform Your Pharmaceutical Operations

IntuitionLabs designs, builds, validates, and operates custom AI agents for pharmaceutical and life-science organizations. From regulatory intelligence to clinical operations, our agents handle the data-intensive work so your experts can focus on the decisions that matter.

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