Luna Bora
Senior Director, MResult
We started this series by laying the foundation of Agentic AI: intelligent systems that shift from task responders to autonomous actors. Then we explored what enterprise readiness really means and why most implementations fail before they scale. Now, we bring it all together: real use cases, implementation wins and misses, and the lessons that separate early promise from lasting value.
When Theory Met the Floor
Here are some of the agents my team and I deployed to solve real business problems—each one evolving beyond automation into autonomy and delivering measurable impact.
Adaptive Summarization for Medical Affairs
The medical affairs function at a global pharmaceutical client was drowning in clinical literature. Our agent was designed to continuously monitor research updates, extract relevant insights, and format summaries for different audiences—medical, regulatory, and compliance. But the breakthrough came when it began adjusting outputs on its own, based on how different stakeholders responded. The loop wasn’t just automated—it was adaptive, learning from use and improving without reprogramming
Smart Triage in Patient Support
In a digital health platform, patient queries—ranging from lab reports to next steps—were overwhelming the support team. We introduced an agent that could interpret the query, decide whether to resolve it, escalate it, or reroute it. What started as simple classification evolved into nuanced triage: chronic conditions were answered instantly, unfamiliar symptoms were escalated, and workflows got smarter with use.
Of course, not every rollout goes to plan, like the case of a pharma R&D unit that deployed an agent to generate first-draft content for regulatory filings. It worked well—until it didn’t. Drafts started omitting key jurisdictional clauses. Submissions for Europe were missing region-specific mandates. The agent hadn’t failed in logic—it was acting without contextual guardrails. No escalation rules. No stop conditions. What looked like speed turned into risk. The project paused. The lesson: autonomy without oversight is a liability.
Empathetic Comfort Agents
There is a widening gap between the amount of mental health issues reported globally and the availability of professional care. When Tranquilla AI decided to work on an always available empathetic first responder for mental health issues, they trusted us to come up with a technical solution. We built an agentic platform powered by advanced models with realistic voice integration. A key feature of our solution was the implementation of long-term memory, allowing a variety of empathetic comfort agents to be built that could remember patient backgrounds and provide continued support when it was needed most. This innovative solution to a problem of massive scale was recently recognized at the Aegis Graham Bell Award .
Contextual Scheduling for Healthcare
In a hospital network, scheduling across diagnostics, doctors, and patients was fragmented. We built an orchestration agent that looked beyond availability—it factored urgency, location, and resource constraints to recommend optimal slots. In several instances, it rebalanced facility loads proactively. The outcome wasn’t just smoother scheduling—it was smarter allocation across the system.
Tailored Sales Content on Demand
Sales reps at a global pharma firm needed custom materials for every physician interaction. The agent we introduced accessed CRM data, recent engagements, and user behavior to build personalized decks. Over time, it picked up tone preferences—who liked clinical depth versus summary slides. What began as content automation turned into relationship intelligence.
AI-Powered Post-Call Summaries
At a health insurer’s contact center, call summaries and sentiment tracking were slowing agents down. We deployed a voice-aware agent that transcribed, tagged tone, and auto-filled CRM notes. But soon, it started shaping summaries differently for different users—brief for internal logs, empathetic for client notes, and audit-aligned for compliance. The value wasn’t just in writing faster—it was in writing smarter.
When Autonomy Overreaches: Global Lessons
Even the most advanced deployments can falter when autonomy outpaces oversight. Across industries, there are well-documented instances—discussed in global circles—where agents operated beyond their intended bounds, not from failure in capability, but from gaps in context, control, or governance. These are not failures of AI but lapses in design—reminders that capability without control creates risk. A highly capable agent operating without escalation logic or stop conditions can quietly go off-course.
In one well-publicized case, an agent silently stopped functioning for two days. No alerts. No errors. The issue? An expired credential and a missing monitoring hook. A simple oversight—but with cascading impact. These instances remind us: even the most capable agents need watchful systems around them.
Practices That Set the Pace
From what I’ve seen, five principles separate the programs that scale from those that stall:
- Design for feedback: Agents that improve over time are those connected to real feedback loops—from users, data, or system outcomes.
- Don’t skip human checkpoints: Oversight doesn’t slow you down. It keeps trust intact.
- Start with modular workflows: Inject AI where it can act, not where it has to control everything.
- Treat agents like teammates: Give them clear roles, escalation paths, and accountability.
- Invest in observability: Logging, tracing, alerts—they’re not nice-to-haves. They’re survival.
Delegating with Intelligence
There’s a mindset shift underway in the domain of AI. Early Agentic AI efforts were about reducing effort. The new wave is about transferring ownership. Whether it’s a scheduling agent negotiating time slots or a sales agent shaping narrative, we’re now seeing systems that not only act but decide.
This shift isn’t about replacing humans. It’s about elevating them. When done right, Agentic AI clears the noise so people can focus on nuance, judgment, and strategy.
Operationalizing the Agentic Advantage
Agentic AI is not a tool you deploy. It’s a capability you grow. The best results I’ve seen come from teams that start small, learn fast, and keep humans at the center. The agents don’t need to be perfect. But they do need to be grounded, governed, and given room to evolve.
We’re not just automating tasks anymore. We’re rethinking how work gets done. And if the early signs hold, the future of enterprise productivity won’t be man or machine. It will be both—in sync, in flow, and in partnership.
