Webinar Recap: Agentic Decision Intelligence — The Next Evolution in Autonomous Decisions
Summary
In our Future.Now webinar, “Agentic Decision Intelligence: The Next Evolution in Autonomous Decisions,” we explored how enterprises are moving from traditional automation to intelligent systems that make decisions with speed, context, and intent. We examined the rising operational complexity facing global organizations — more data, shorter cycles, and continuous disruption — and showed how these pressures exceed what manual processes and static rules can manage. We then demonstrated how Aera, the decision intelligence agent, aligns decisions to business goals, evaluates trade-offs, and learns from every outcome to deliver faster, more resilient performance across planning and operations.
We walked through how specialized AI agents interpret live signals across supply, demand, and financial indicators; assess risks; simulate possible actions; and provide transparent recommendations for teams to review or automate. A live demonstration illustrated how Aera connects data, models, and workflows into closed-loop decision cycles that continuously adapt to change. We closed by showing how organizations using agentic decision intelligence are improving accuracy, reducing manual workloads, and accelerating enterprise response, ultimately creating a more agile, digitally empowered operating model.
Key Takeaways
- Goal-driven autonomy outperforms traditional automation.
Automation speeds up tasks but struggles when conditions shift or decisions require balancing objectives. Agentic decision intelligence introduces systems that operate based on business intent, constraints, and expected outcomes. Decisions are shaped by what the enterprise is trying to achieve — such as service, margin, throughput, and cost — rather than hard-coded rules. This shift allows organizations to navigate complexity with greater confidence and adapt far faster to real-world change. - Decision agents evaluate options with full operational context.
Agents continuously monitor demand patterns, supply signals, inventory positions, and policy thresholds, surfacing issues before they escalate. They assess alternative actions, simulate impact, and present options with quantified outcomes. This ensures that decisions reflect real constraints — in terms of materials, capacity, customer priorities, and financial targets — and that recommendations are both targeted and explainable. Teams gain a structured way to compare options, reduce guesswork, and act more consistently. - A no-code environment accelerates how agents are built and orchestrated.
Aera’s visual tooling makes it possible to design, configure, and adjust decision agents without specialized engineering work. Users can map data sources, define decision logic, test workflows, and orchestrate multiple agents as coordinated teams. This reduces time-to-value and broadens participation, enabling business and technical stakeholders to collaborate on building autonomous capabilities that evolve with organizational needs. - Governance, transparency, and trust are built into the decision lifecycle.
Every recommendation produced by Aera includes a clear rationale, data lineage, confidence level, and an audit trail of the factors that influenced the decision. Policies and approval workflows ensure that autonomy scales safely across functions. This creates a reliable foundation for widespread adoption, providing teams with visibility into how decisions are made and confidence that actions align with business rules, compliance requirements, and operational standards. - Continuous learning strengthens decision quality over time.
Aera captures the outcomes of each decision, evaluates whether expectations were met, and uses those insights to refine thresholds, heuristics, and future recommendations. Over time, agents become more accurate, faster to respond, and better aligned with enterprise goals. This transforms decision-making from periodic adjustments and manual tuning into an ongoing improvement cycle that raises performance across planning, forecasting, and execution.
Speakers
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Mustafa Kabul, SVP of Data Science, Machine Learning, and AI, Aera Technology. Mustafa works at the intersection of machine learning, optimization, and generative AI, where he develops the intelligence that powers Aera, the decision intelligence agent. He focuses on advancing the models, heuristics, and learning systems that enable Aera to deliver fast, accurate, and goal-aligned decisions at enterprise scale. |
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Pete Quimby, Northeast Regional VP of Sales, Aera Technology. Pete brings over 25 years of experience in sales and organizational leadership across manufacturing, engineering, and information technology within the Fortune 250. He focuses on helping enterprises adopt Aera’s decision intelligence capabilities to drive measurable improvements in operational efficiency, resilience, and autonomy. |
Full Recording
Access the full webinar recording here.
Q&A
At the end of the presentation, we held a short session dedicated to answering attendees’ questions. Below are several of the key questions and answers.
Q: How is Aera’s agentic decision intelligence different from Microsoft Azure AI Foundry?
A: Aera is designed as a decision intelligence platform from the ground up, with every component built to support high-stakes enterprise decision-making. The agents created in Aera — whether individual autonomous agents or coordinated teams — are purpose-built to tackle complex decision problems and continuously improve through learning. This framework brings the power of large language models into a structured, decision-centric environment, ensuring that enterprise decisions are made accurately, consistently, and with full context.
Q: What are the typical use cases of agentic decision intelligence?
A: Agentic decision intelligence supports a range of real-world use cases that enable organizations to automate, augment, and optimize decisions across functions. Common examples include:
- Order Allocation (Available to Promise — ATP)
Continuously evaluates inventory availability, customer priorities, and delivery constraints to confirm orders quickly and accurately using real-time data. - Demand and Supply Response
Detects sudden demand shifts or supply disruptions and initiates rapid rebalancing actions by analyzing live inventory, demand inflows, and network constraints. - Demand Planning
Generates dynamic, AI/ML-driven forecasts by integrating historical sales, promotions, and external signals, replacing static, spreadsheet-based planning. - Manufacturing Exception Management
Monitors production signals to detect issues such as material shortages or equipment failures, recommending prioritized corrective actions before they affect downstream operations. - Inventory Command Centre
Provides a unified, real-time view of inventory across the network and automates actions to resolve imbalances, shortages, excesses, and aging stock.
Q: What’s a realistic timeline to deliver these use cases in production?
A: Timelines vary by customer, but organizations adopting agentic decision intelligence are seeing significant acceleration. Each implementation depends on the specific use case and operational environment, so timing is best evaluated on a case-by-case basis. Recent examples shared at AeraHUB highlight how agentic approaches substantially shorten delivery timelines compared to traditional deployment models.
Poll
During the webinar, attendees were asked the following poll question: “Now that we’ve explored agents and agent teams, which capability do you think creates the biggest impact?”
The answer options included:
a) Faster design and construction of complex decision workflows
b) Collective intelligence from multiple specialized agents
c) Agent functions for reusable and scalable execution
d) Enhanced reasoning abilities
The majority of attendees selected a) Faster design and construction of complex decision workflows and b) Collective intelligence from multiple specialized agents, underscoring the value organizations place on speed, composability, and coordinated intelligence when scaling autonomous decision-making.
