Webinar Recap: Agentic Decision Intelligence: Orchestrating AI Agents That Decide, Act, and Deliver

Summary

In our Future.Now webinar, Agentic Decision Intelligence: Orchestrating AI Agents That Decide, Act, and Deliver, we explored how organizations are moving beyond AI experiments and into reliable AI that generates real business value. Many companies have invested heavily in AI, yet still struggle to turn insights into action. AI models often operate in isolation, outside day-to-day workflows, and lack coordination, trust, and clear explanations for how decisions are made. As a result, decisions remain slow, manual, and difficult to scale across the enterprise.

The webinar focused on how agentic decision intelligence helps close these gaps. By coordinating AI agents and agent teams directly within live decision workflows, organizations can connect data, reasoning, and execution in a single system. Agents can work with both structured and unstructured data, evaluate trade-offs, and take action, all while remaining transparent, governed, and aligned with business rules and controls.

Using platform demos and real examples, we showed how agents can be built quickly, tested through simulations, and refined over time. When deployed at scale, this approach enables faster decisions, quicker deployment, and measurable business results.

Key Takeaways

• AI value breaks down when models operate in isolation.
  Many organizations still face an integration gap: AI pilots can produce insights, but those insights are not embedded in the workflows where decisions are made and executed. When recommendations live outside business processes, adoption stalls and value stays theoretical. Agentic decision intelligence closes this gap by placing agents inside live decision workflows, where they can trigger actions, support users, and drive execution.
• Agentic decision intelligence depends on orchestration, not just models.
  Complex enterprise decisions rarely fit inside one model’s capabilities. LLMs can reason over text, but they are not enough on their own for optimization, forecasting, or structured decision execution. Orchestration brings multiple AI capabilities together (such as machine learning, optimization, reasoning, and automation) so decisions can be handled end-to-end. This is what makes agentic AI practical for real operational complexity.
• Unstructured data expands what can be sensed, checked, and acted on.
  Agentic capabilities make it possible to incorporate unstructured inputs (such as supplier emails, contracts, PDFs, and even meeting summaries) into decision processes. That opens new ways to detect risk early, trigger workflows, and run feasibility checks that were hard to automate before, such as identifying potential contract violations or interpreting disruption signals. The result is decision-making that is more relevant to the broader context.
• Generative decision reasoning reduces build effort and improves adaptability.
  Many decision processes depend on complex decision trees that take time to code, maintain, and update, especially when rules differ across regions or business units. With generative decision reasoning, that logic can be expressed in natural-language prompts, then adjusted quickly as conditions change. Instead of rebuilding and redeploying code, prompts can be refined, tested, and improved in a tight loop to accelerate time to value.
• Trust is earned through controls, explainability, and observability.
  Adoption slows when AI feels like a black box. Enterprise decision-making requires transparency, governance, and clear boundaries on autonomy. Trust is strengthened through certified data and logic, control-by-design guardrails, and end-to-end explainability where every decision can be simulated, tested, and traced. Real-time observability (including inputs, reasoning steps, and outcomes) supports both accountability and continuous improvement.
• Agent teams enable scalable, cross-functional problem solving.
  Many decisions span multiple functions and require specialized expertise that no single agent should attempt to cover alone. Agent teams address this by coordinating reusable agents running them sequentially, in parallel, or with dependencies, and then synthesizing outputs into a final recommendation. This approach speeds development, encourages reuse, and helps connect teams that traditionally operate in silos, supporting broader enterprise-scale execution.

Speakers

	Suraj Ramalingam, Senior Solutions Engineer, Aera Technology Suraj brings deep experience in global supply chain transformation, with prior leadership roles at Salesforce and Procter & Gamble. At Aera Technology, Suraj focuses on helping enterprises operationalize decision intelligence by designing and deploying AI-driven decision workflows that deliver measurable business outcomes.
	John Coffey, Solutions Engineer, Aera Technology John has over a decade of experience in AI, decision intelligence, and enterprise automation. Before joining Aera Technology, John spent seven years at EY delivering technology and process intelligence solutions. At Aera, John has led major customer implementations, including the company’s first end-to-end agent-orchestrated skill.

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 does the orchestration take place across multiple systems of execution within a company’s vast end-to-end ecosystem?

A: The key is integrating the different systems of execution with Aera Decision Cloud. Aera can ingest data from those systems, and it can also write transactions back to each of them. The advantage is having the intelligence in one system, so the correct decision is made before actions are written back to the various systems of execution.

Q: What is the secret sauce for successful agent orchestration? Can you share more detail?

A: The core elements are the right data, the right decision engines, and the right guardrails. When these come together, orchestration becomes possible at scale.

Q: How do the roles and expertise of the agent influence the guardrails and outcomes?

A: An agent’s role and domain expertise directly define its guardrails and outcomes. The role determines which data, decision logic, constraints, and actions the agent can access, while domain expertise embeds business rules, objectives, and risk tolerances. This ensures agents make context-aware, explainable decisions and act only within approved boundaries, producing outcomes aligned with business intent and governance.

Q: Do these agents remove the need for decision trees or rule-based automation?

A: In some simpler cases, yes. In other cases, Aera agents incorporate rule-based logic such as decision trees, thresholds, and constraints as inputs. Agents combine rules with machine learning, optimization, simulation, and heuristic learning, allowing decisions to adapt to context, scale, and change over time. In practice, rules become guardrails, while agents handle reasoning, trade-offs, and continuous improvement beyond static automation.

Q: Can you advise if any companies have already applied this solution, and what use cases were implemented?

A: There are several areas where this has already been deployed, including claims management, shortage identification and mitigation, and multiple root cause analysis use cases such as spend increases, shortages, excess inventory, and forecast changes. Additional use cases include purchase order change management, where agents read emails and trigger changes and mitigation actions.

Q: Do agent functions need to be defined, or are there out-of-the-box functions available?

A: Aera provides out-of-the-box agent functions for common decision patterns. At the same time, teams can define and extend custom functions to support specific business logic and requirements.

Q: Could you provide more clarity on cost and how clients can estimate it?

A: Aera is priced as a platform subscription, not per agent or per decision. For detailed pricing and cost estimates tailored to a specific use case, we encourage you to contact us directly.

Q: Do Fortune 500 customers end up replacing or decommissioning other software, and what is the impact on competencies and people plans?

A: Aera has helped some customers simplify and declutter their technology stacks. The impact on competencies and staffing plans is significant, because Aera removes a large amount of manual work. This allows teams to operate at their maximum capability, using their expertise instead of focusing on repetitive tasks that consume time.

Q: Why choose Aera?

A: As the first decision intelligence agent, Aera goes beyond providing insights by combining real-time data, agentic orchestration, execution, and learning to automate decisions with speed, scale, and measurable outcomes.

Q: How is Aera’s agentic decision intelligence different from what others are doing in the agentic space?

A: Aera’s agentic decision intelligence is purpose-built for decisions, not just agents. Unlike most agentic platforms that focus on task automation or workflow assistance, Aera embeds agents into a closed-loop decision system grounded in the Decision Data Model™, simulation, decision engines, execution, and learning. Aera agents sense, decide, act, and learn in real time, with governed autonomy and human oversight, delivering faster time to value, repeatable decision automation, and continuously improving outcomes, all at enterprise scale.

Q: Can you talk more about Aera’s decision engines and the platform overall?

A: Aera Decision Cloud™ is designed to move beyond insight into decision execution and learning. At its core are decision engines that combine machine learning, forecasting, optimization, business rules, graph reasoning, and simulation to analyze and execute each decision.

What makes Aera different is how these engines operate together on top of the Decision Data Model™, a real-time semantic layer that unifies enterprise data and records every decision and outcome. This enables Aera to sense changes, simulate options, recommend or act, and learn continuously in a closed loop. The platform is fully composable and agentic, with decision engines powering Aera Skills™ and agents deployed with varying levels of autonomy and human oversight. The result is an enterprise-scale system that delivers fast time to value, transparent decision logic, and continuously improving decisions across industries.

Q: How quickly did organizations see results using Aera agents, and what does the implementation timeline typically look like?

A: Customers typically see value very quickly with Aera agents. An initial environment can be stood up and validated with customer data in four to eight weeks, including the Decision Data Model™ and the first skill and agent flows. From there, customers expand incrementally by adding new agents and decisions without re-architecting, allowing value to compound over time.

Q: Are any agents being implemented for supply chain use cases?

A: Yes. Aera agents are actively implemented for core supply chain use cases, including demand and supply balancing, inventory balancing, safety stock optimization, order prioritization, and exception management. These agents continuously sense real-time signals such as demand shifts and supply disruptions, simulate trade-offs, and recommend actions.