Agentic AI

What is agentic AI?

Agentic AI refers to AI systems with autonomous, goal-driven, and decision-making capabilities that can plan, execute, evaluate, and refine their actions with minimal human intervention. Unlike traditional AI models that are passive and task-specific (e.g., predictive or analytical models), Agentic AI possesses agency—the ability to reason, learn, act, and adapt dynamically. It is particularly transformative in domains like drug discovery, bioinformatics, scientific informatics, and clinical research, where complex workflows require intelligent automation, contextual understanding, and cross-functional data integration.

In the context of Excelra’s offerings, Agentic AI complements and enhances core capabilities such as data curation, bioinformatics services, scientific data management, cloud enablement, and AI-ML-powered drug discovery. By incorporating autonomous reasoning, domain-specific knowledge, and multi-agent collaboration, Agentic AI significantly accelerates time-to-insight, reduces manual intervention, and enhances the accuracy and quality of decision-making across the R&D value chain.

Core Components of Agentic AI Systems

1. Autonomous Decision-Making

Agentic AI agents can operate independently, evaluating scientific goals, selecting optimal workflows, and executing bioinformatics or cheminformatics tasks without manual intervention.
In Excelra’s context, they may autonomously:

• Select genomic datasets from internal repositories, PubChem, or GEO.
• Decide whether to run GSNAP, FASTQC, or HISAT2 based on dataset types.
• Generate structured analytical reports for pharmacophore modeling or biomarker discovery.

2. Goal-Oriented Intelligence

Designed to optimize outcomes over time, Agentic AI systems break down high-level scientific or clinical objectives—such as “Identify novel biomarkers for NSCLC”—into actionable workflows. They use reinforcement learning, feedback loops, and strategic planning to improve performance and align with scientific goals.

3. Advanced Planning and Reasoning

Agentic AI goes beyond data processing; it strategically reasons through biological contexts using probabilistic modeling, symbolic logic, and decision trees. For example:

“If genetic markers X and Y are linked to patient non-response, evaluate molecular docking predictions for alternative drug candidates.”
This enables downstream decision-making insights for drug development, repurposing, and clinical strategy.

4. External Tool & Workflow Integration

Excelra’s scientific ecosystem involves multiple tools—Nextflow pipelines, ONT platforms, molecular docking engines, LIMS, ELNs, and real-world data platforms.
Agentic AI can:

• Connect to APIs, cloud-based HPC pipelines, or laboratory infrastructure.
• Run RNA-seq, variant analysis, MD simulations, and literature mining workflows.
• Integrate insights across Excelra’s proprietary knowledge bases (GOSTAR™ , GOBIOM, and EBM).

5. Contextual Memory & Scientific State Tracking

A powerful capability of Agentic AI is its ability to retain context, track ongoing research states, and maintain experiment continuity.
For example:

• Remembering prior biomarker hypotheses.
• Tracking pharmacophore validation results.
• Retaining metadata from previous workflow runs.

This enables long-term, iterative, and intelligent scientific execution—similar to a knowledgeable research collaborator.

Key Characteristics of Agentic AI

How Agentic AI Differs from Traditional AI

Applications of Agentic AI in Drug Discovery & Life Sciences

Agentic AI can autonomously navigate complex R&D workflows—connecting data, tools, and insights across Excelra’s scientific informatics, bioinformatics, and data services. Below are high-impact use cases:

1. Agentic AI in Bioinformatics

Agentic AI enhances NGS data analysis, biomarker discovery, pipeline development, and omics data interpretation by proactively optimizing analysis steps, detecting errors, and making informed decisions. For example, it can evaluate multiple alignment strategies, suggest better QC thresholds, or even simulate biological responses.

It augments Excelra’s Bioinformatics Solutions and capabilities in disease landscape analysis, pipeline development, and genetic biomarkers interpretation.

2. Agentic AI in Drug Repurposing

Within Excelra’s Drug Repurposing Solutions, Agentic AI can autonomously:

• Explore databases like GOSTAR™ and scientific publications.
• Identify new therapeutic indications.
• Evaluate safety, efficacy, and biomarker associations.
• Predict drug-target interactions and real-world evidence.

It transforms the traditional search-based approach into an insight-driven, intelligent agent workflow.

3. Intelligent Data Curation & Scientific Informatics

• Agentic AI revolutionizes data curation, traditionally a manual and time-intensive task. It can:
• Extract entities (genes, biomarkers, compounds) from literature.
• Validate, annotate, and standardize metadata using ontology-based AI agents.
• Ensure FAIR compliance (Findable, Accessible, Interoperable, Reusable).

It supports Excelra’s strengths in data structuring, healthcare data management, cloud-enablement, and scientific data management by turning static datasets into evolving knowledge graphs.

4. Agentic AI for Clinical Development

In clinical development and modeling, Agentic AI agents can:

• Simulate patient populations.
• Design adaptive clinical trials.
• Optimize dose regimens using QSP and PK/PD modeling.
• Automate safety reporting and regulatory documentation.

This directly connects with Excelra’s services like Clinical Data Services, IQSP, and PK/PD modeling.

Benefits of Agentic AI for Scientific and Clinical Research

Excelra’s Role in Enabling Agentic AI

• Excelra’s strengths in data, analytics, platforms, and cloud infrastructure create the foundation for developing Agentic AI frameworks:
• GOSTAR™ and GOBIOM databases supply high-quality structured data.
• Scientific Application Development enables customized AI-integrated systems.
• Cloud Enablement & Infrastructure supports scalable agent-based models.
• Data Curation and Semantics Data Services enable FAIR, ontology-driven data connectivity.
• Bioinformatics & AI-ML Consulting provide domain training for AI agents.

Future of Agentic AI in Excelra Ecosystem

Agentic AI will reshape how scientists, researchers, and pharmaceutical companies interact with data, insights, and scientific workflows. Future-ready AI agents will:

• Automatically build disease landscape reports.
• Recommend drug combinations and repurposing candidates.
• Participate in interactive scientific reasoning and advisory roles.
• Collaborate with humans in multi-agent research ecosystems.

With Excelra’s vision of transforming scientific data into actionable intelligence, Agentic AI is not just a technology trend—it is the next evolution in smart research and evidence-driven decision-making.