How are AI agents changing the software industry?

AI agents represent a fundamental shift, creating a new layer that can potentially 'eat software away' by moving business logic from traditional applications into a more abstract agentic layer.

What's the difference between building and running AI agents?

Building agents is often the 'tax' paid for development, with negative ROI initially. The real value and positive ROI come from running agents in production, where they deliver continuous value.

What challenges are companies facing when deploying AI agents?

Companies face both AI-related challenges like evaluation and guardrails, and non-AI related engineering problems such as SSO, RBAC, and deployment on their own infrastructure.

What is an agentic system pattern?

An agentic system combines a deterministic backbone (like a flowchart) with the option to opt-in agency at specific steps, allowing for complex tasks to leverage multiple agents within a structured flow.

What are best practices for sharing context between agents?

Best practices include using agent repositories for reusable agents and tools, and implementing robust memory systems like RAG or Graph RAG for persistent context and long-term memory.

Can AI agents simulate group conversations or debates?

Yes, AI agents can simulate group discussions. Often, this involves a single entry point that routes requests to multiple agents working behind the scenes to create a cohesive, multi-agent conversation.

Key Moments

AI Dev 25 x NYC | João Moura: Design, Develop, and Deploy Multi Agent Systems with CrewAI

DeepLearning.AI

Education4 min read30 min video

Dec 4, 2025|739 views|15|2

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Key Moments

TL;DR

AI agents are revolutionizing business logic, moving it to a new layer. Focus shifts from building to running agents for true value.

Key Insights

AI agents represent a fundamental shift, creating a new layer for business logic that could "eat" traditional software.

The true value of AI agents lies in their deployment and operation, not just in building them; ROI is negative during development.

Enterprises are increasingly mandating AI agent deployment for real-world applications due to their ability to create, react, and take action.

A new pattern of "agentic systems" is emerging, combining deterministic flows with optional agentic agency for complex tasks.

The focus for enterprise adoption is shifting towards centralized control (CIO, CTO) managing guardrails, settings, and data privacy.

Key challenges in agent deployment include robust evaluations, guardrails, and traditional engineering problems like SSO and Rback.

THE EXPLOSIVE GROWTH AND POTENTIAL OF AI AGENTS

The current landscape of AI agents is experiencing unprecedented growth, with billions of agentic executions occurring quarterly. This rapid expansion, facilitated by platforms like CrewAI, underscores the immense potential and increasing adoption of these intelligent systems. The CEO of CrewAI highlights that while the numbers are staggering, they represent only a fraction of the future possibilities, indicating a significant untapped market and technological frontier.

DEFINING AI AGENTS: AGENCY AND ACTION CAPABILITIES

At their core, AI agents are advanced systems built upon Large Language Models (LLMs) that possess agency. Unlike basic LLMs adept at content creation, agents can make decisions, utilize tools, and execute actions to achieve defined goals. This ability to autonomously decide whether to call a tool, pull data, or push information into systems forms the foundation of their power, enabling developers to orchestrate complex application flows.

AI AGENTS AS A FUNDAMENTAL BUSINESS LOGIC SHIFT

Technological shifts historically reposition business logic, moving it to new platforms like desktop apps, web services, or cloud APIs. AI agents are posited as the next major evolution, representing a new layer that could fundamentally alter how software is built and operated. This perspective suggests that agents are more than an engineering project; they are poised to become the new 'house' for business logic, potentially consuming significant portions of existing software systems.

REAL-WORLD IMPACT THROUGH AUTOMATED PROCESSES

The practical application of AI agents is already demonstrating significant value across industries. Use cases include drastically reducing the time to validate reimbursement requests from three days to ten minutes in CPG companies, and accelerating Know Your Customer (KYC) processes in financial institutions from one week to 15-30 minutes, resulting in a four-fold increase in speed. These examples highlight the tangible efficiency and cost-saving benefits agents deliver.

SHIFTING FOCUS FROM BUILDING TO RUNNING AGENTS

While many tools focus on simplifying the building and prototyping of AI agents, the real value is realized during their deployment and operation. The return on investment (ROI) for building agents is initially negative, as it requires spending time and resources without immediate returns. The value proposition fully emerges when agents are running in production, completing tasks and generating tangible outcomes. This underscores a critical gap between the ease of building and the complexity of operationalizing agents.

THE EMERGENCE OF AGENTIC SYSTEMS AND DEPLOYMENT STRATEGIES

A prevalent pattern observed is the development of "agentic systems," which blend deterministic, sequential flows with flexible agentic agency. This approach allows for a structured backbone that can dynamically incorporate agents for specific, complex tasks requiring higher levels of autonomy, such as deep research or analysis. This hybrid model offers a more nuanced approach to agent design, moving beyond pure agent crews or simple flows.

ENTERPRISE ADOPTION AND OPERATIONAL CHALLENGES

Large enterprises are increasingly adopting AI agents, with adoption often centralized under CIOs or CTOs. This controlled approach aims to manage guardrails, settings, and data privacy effectively. However, significant challenges remain, including the need for robust evaluations, proper guardrails, and addressing traditional engineering requirements like single sign-on (SSO) and role-based access control (RBAC) before widespread deployment.

THE ROLE OF PLATFORMS AND OPERATIONAL LAYERS

The AI agent ecosystem comprises various layers, from cloud providers and data sources to LLMs and a new operational layer for agents. Platforms are emerging to manage these agents, focusing on key areas like building, integration, observation, optimization, and scaling. This operational layer is crucial for enabling organizations to move from initial experimentation to trust and value delivery, supporting features like memory, tools, and knowledge management.

IMPROVING AGENT COLLABORATION AND MEMORY

Addressing challenges in shared context and persistent memory between agents is vital. Features like agent repositories allow for reuse of agents and tools across an organization, preventing redundant development. For long-term memory, techniques such as Retrieval-Augmented Generation (RAG) and graph RAG are being employed as robust backbones to enable agents to retain information across multiple executions, thus avoiding repetitive research and tasks.

FUTURE DIRECTIONS AND COURSE OFFERINGS

The field of AI agents is rapidly evolving, with emerging patterns like agentic systems gaining traction. CrewAI, as a key player, offers resources like courses and certifications to help individuals and organizations navigate this complex landscape. The emphasis is on practical application, moving from understanding the technology to effectively designing, developing, and deploying agents that deliver significant business value.

Mentioned in This Episode

●Software & Apps

●Companies

●Concepts

●People Referenced

Designing and Deploying AI Agent Systems

Practical takeaways from this episode

Do This

Focus on the value of running agents, not just building them.

Consider agentic systems that blend deterministic flows with optional agency.

Leverage existing tools and frameworks, but avoid over-engineering (KISS).

Utilize agent repositories for reusability across teams and projects.

Implement robust memory systems (short-term, long-term, entity-based) using RAG or Graph RAG.

Prioritize central adoption under IT leadership (CIO, CTO) for guardrails and security.

Build trust and focus on delivering value in agent deployment.

Avoid This

Don't get stuck in the building and prototyping phase; value comes from production.

Don't build agents if a simpler solution suffices (KISS principle).

Don't overlook traditional engineering problems like SSO and RBAC for enterprise deployment.

Don't assume early memory implementations were robust; mature approaches are needed.

Don't create separate interfaces if using tools like CrewAI that offer integrated experiences.

Common Questions

An AI agent is an LLM with agency, capable of making decisions and taking actions to achieve a goal. They are important because they enable new forms of automation, creation, and real-time reactivity in applications.

Topics

CrewAI Business Logic Deployment Agentic Systems KYC

Mentioned in this video

Concepts

Graph RAG

A type of RAG that uses graph structures, mentioned as a backbone for implementing agent memory.

Software & Apps

CrewAI

A tool for designing, developing, and deploying multi-agent systems. The speaker is a proponent of CrewAI and discusses its scale, features, and evolution.

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