Building agentic AI that actually works in the real world

If the first generation of AI amazed us with fluent text and impressive demos, the next one will be defined by something far more practical: actually getting things done. For pharmaceutical and biotech companies, this is not theoretical, it’s a core operational requirement. Real-world AI in pharma must go beyond clever conversation. It must reason across regulatory steps, learn from study data, connect to live clinical systems, and track its progress in producing accurate, auditable content.

That’s what agentic AI is all about. And through our work with global pharma leaders, Narrativa has proven that agentic systems can bring real transformation to areas like Clinical Study Report (CSR) generation, patient narratives, redaction, and safety monitoring. But to make it truly work, we need to give AI four superpowers: the ability to remember, to learn, to connect, and to pursue clear goals. Without these, even the most advanced model is just a smarter chatbot.

Let’s explore how these four capabilities combine to create AI that performs reliably, adapts intelligently, and delivers tangible results in pharmaceutical environments.

Remembering what matters

If you’ve ever been on a customer service chat that forgot what you said two messages ago, you already know why memory is critical. In regulated domains like pharma, this becomes even more essential. An AI agent generating multiple narratives across studies must maintain context, not just within a session, but across projects.

Large language models come with short-term “working memory,” the context window. It holds recent conversation history and instructions. But once it’s full, older information disappears. In pharma, where long-running tasks and cross-document consistency are crucial, this isn’t enough.

To support real-world clinical workflows, agentic AI needs two layers of memory:

• Short-term memory acts like a working scratchpad, holding outputs from clinical data extraction tools, recent validation feedback, and event classification outcomes.
• Long-term memory stores persistent knowledge such as therapeutic area guidelines, sponsor-specific templates, and prior narrative decisions.

Narrativa® Navigator leverages both layers. For example, our Narrative Pathway solution can maintain memory across thousands of patient narratives, ensuring consistency in terminology and event classification. The power lies not in storing everything, but in retrieving exactly what matters when needed.

Watch how Narrativa® Narrative Pathway works

Learning and adapting over time

Once an agent remembers, the next step is helping it improve. Regulations evolve, sponsor preferences shift, and writing styles vary by geography or reviewer.

Therefore, agentic AI must continuously adapt. At Narrativa, our solutions employ learning mechanisms such as:

• Preference learning to align output tone and structure with sponsor requirements.
• Policy refinement to optimize decision trees across multi-step clinical workflows.
• Knowledge expansion as new regulatory standards emerge (for example, the E3 standard, which is intended to harmonize the format and content of the clinical study report, or ICH E6, which relates to Good Clinical Practice).
• Procedural updates that adapt how narratives are generated based on reviewer feedback or audit findings.

This approach turns every clinical writing interaction into training data. For example, Sidekick, our co-writing tool, tracks editorial changes and evolves its suggestions. Similarly, our CSR automation module refines its template selection and source referencing logic based on success metrics.

Connecting to the world with the Model Context Protocol (MCP)

An agent that can remember and learn still needs one final ingredient: action. To be operational in the pharma world, AI must connect with trial databases, regulatory repositories, and internal knowledge systems. That’s where the Model Context Protocol (MCP) comes in.

MCP is an open standard that enables interoperability between large language models and external systems. It gives AI the ability to retrieve, process, and act on live data through structured access to:

• Resources, such as CDISC datasets, MedDRA dictionaries, or protocol documents.
• Tools, like medical dictionary lookups, adverse event classification engines, or submission checkers.
• Prompts, which define how the agent should use these tools in regulated environments.

Narrativa’s pharma deployments use MCP-like architecture to orchestrate tools like Redaction Scout or TLF Voyager within a unified agent workflow. This allows AI to synthesize multiple clinical data sources and regulatory requirements into compliant, validated output.

With MCP, the agent doesn’t just suggest text, it acts on real inputs, validates with tools, and prepares documentation for submission. It bridges the gap between language and action.

Giving AI a sense of direction

Even the smartest agent, fully connected and adaptive, is ineffective without direction. Pharma teams require clarity and traceability at every step, which is why agentic AI must operate with clear, measurable goals.

Just like a regulatory writer is tasked with producing a submission-ready CSR or resolving a safety case, the AI needs to understand:

• The current state (e.g., data gaps, unaddressed adverse events).
• The desired goal (e.g., a complete, compliant document).
• How to track progress (e.g., through checklists, accuracy metrics, or validation passes).

Narrativa agents apply this through structured planning and monitoring. For instance, during CSR generation, the system defines stages—data extraction, narrative generation, quality validation—and tracks progress using metrics such as completion rate, consistency checks, and compliance thresholds.

This makes the system not just reactive, but proactive, capable of adjusting strategies, escalating issues, or requesting clarification when needed.

Bringing it all together

When memory, learning, connection, and goals align, AI transforms from a passive assistant into an active contributor. That means:

• Narratives evolve with clinical insight.
• Reports self-correct based on past reviewer feedback.
• Agents pull live data from CDMS or EDC systems, process it, and output usable content.
• Tools know the target, like regulatory readiness or safety case resolution, and work purposefully toward it.

Our clients already use these capabilities to reduce documentation timelines, increase output consistency, and scale quality across regions and teams. Narrativa’s agentic AI solutions build systems that think, learn, connect, and achieve. That’s what real-world AI looks like in pharma, and it’s only the beginning.