Building AI Agents for Real-World Problems & Workflows

[00:00] Today, there's a lot of excitement around AI agents.

[00:03] We've seen impressive demos of agents that plan, reason,

[00:13] and act across tools.

[00:16] But the real question isn't whether we can build them or not.

[00:20] The real question is what it takes to make an AI agent effective in real-world environments.

[00:26] When agents move from demo into production systems,

[00:30] many fall short, not because technology is incapable,

[00:34] but because real-world problems are complex, constrained, and interconnected.

[00:40] So instead of focusing on what AI agents are, I want to focus on how they behave in practice

[00:46] when embedded into real world systems.

[00:49] Most real world agent problems share the same core challenges.

[00:54] The first one, they span across multiple systems.

[01:00] Second one, they involve a lot of policies, approvals, and rules,

[01:06] they must fit into existing workflows,

[01:11] and the human should always be in the loop.

[01:16] Because of this, successful agents aren't standalone decision makers.

[01:21] They act like coordination layers,

[01:24] maintaining context, orchestrating actions across systems, enforcing

[01:29] rules, and determining when control needs to be transitioned to a human.

[01:35] A common pattern in real-world agent system is coordinating a sequence of

[01:40] actions across multiple systems while managing state,

[01:48] timing,

[01:51] rules, and exception.

[01:56] This pattern shows up anywhere a single event triggers a multi-step workflow with dependencies.

[02:04] One concrete example of this pattern is onboarding a new employee.

[02:11] Onboarding isn't an easy task.

[02:14] It's a workflow composed of many steps, starting with provisioning, access and entitlements,

[02:21] ordering required resources, scheduling initial activities, assigning required trainings and tracking them to completion.

[02:32] In this use case, agents don't replace people.

[02:37] It uses context-based signals such as roles, location, and start date to sequence actions across systems,

[02:47] monitor workflow state, and flag deviation from expected behavior.

[02:52] The hard part isn't reasoning.

[02:54] It's reliably orchestrating multiple systems while respecting policy and timing constraints.

[03:02] Another recurring pattern is policy-governed action execution, where risk, rules, and access control shape

[03:11] what actions a system is allowed to take.

[03:14] This pattern appears whenever a system is handling incoming requests with very level of sensitivity or impact.

[03:23] IT support is a good representation of this pattern.

[03:26] In this case, agent may process requests such as passwords, software

[03:33] or hardware resources, any requests that come through,

[03:40] ticketing, and routing of any requests.

[03:45] Some requests follow a well-defined and low-risk execution path.

[03:50] Others require validation, approval, and sometimes escalations.

[03:55] An effective agent in this case interrupts requests intent, evaluates the applicable policies,

[04:04] automatically executes some of the permitted actions, escalates any ambiguous or high-risk cases.

[04:14] This shows the explicit control boundaries.

[04:18] The system behaves predictably and humans step in precisely where the rules need them to.

[04:25] In other cases, agents operate inside a well-defined processes where exceptions are the real challenge.

[04:33] This pattern shows up in systems such as invoice processing or order management.

[04:39] In this case, an agent may,

[04:41] extract

[04:45] structural data, match it against the existing record, validate it

[04:56] against rules or concerns,

[04:58] or route approval and lastly, update the downstream systems.

[05:08] This is a happy path.

[05:11] Which is straightforward.

[05:13] The real complexity lies in handling missing data, mismatch data, or any non-standard conditions.

[05:24] Agents add value by consistently handling predictable flows and surfacing only through exception for human reviews.

[05:33] Another important pattern involves triaging and routing large volumes of incoming work.

[05:40] This pattern appears wherever the system needs to prioritize attention under load.

[05:46] A customer service is a great example for this.

[05:50] Here an agent

[05:52] must analyze

[05:58] and categorize incoming requests.

[06:02] Route

[06:05] work to the appropriate teams,

[06:07] and suggest responses based on historical data.

[06:15] Humans still resolve the issues, but agents ensure the priority, context, and routing decisions are applied consistently at scale.

[06:26] The pattern holds regardless of where the work originates.

[06:30] Across all the patterns that we saw, regardless of the domain, the same characteristics apply.

[06:38] A successful AI agents are narrowly scoped, they orchestrate across systems, apply rules, and relate its signals.

[06:56] Keep human in the loop and are designed for integration and not isolation.

[07:05] These systems don't feel like flashy AI features.

[07:09] They feel like well-designed components of a larger architecture.

[07:13] The real power of AI agents is in the autonomy.

[07:17] It's its alignment with real workflows, limits, and control structures.

[07:23] When agents are designed around coordination, rules and accountability.

[07:29] They stop being experiments and start operating as reliable components in production systems.

[07:36] That's what it takes to make AI agents work in the real world.