From: aidotengineer
Organizations are rapidly approaching a future where AI agents will significantly influence operations. It is predicted that in three years, organizations might be run by AI agents, given the current pace of AI development 00:50:02.
Understanding AI Agents
An AI agent is an application built on a large language model (LLM) that exhibits autonomy in its behavior 01:08:11. These agents combine an LLM with specific tools, executing tasks in a step-by-step, planned fashion 01:22:04. Their use of tools can be dynamically adjusted based on previous outcomes 01:37:37, a capability known as dynamic action 01:41:45.
Key Capabilities of AI Agents
AI agents introduce new levels of capability previously almost unique to humans 01:47:47:
- Reasoning for multi-step planning 02:26:40
- Adaptability for dynamic work control 02:28:32
- Persistent memory 02:32:05
- Contextual knowledge through Retrieval Augmented Generation (RAG) for specific, non-LLM knowledge 02:35:40
- Interactive workspaces (sandboxes or canvas) for collaborative refinement of tasks like code or art 02:49:51
- Computer vision agents that can navigate graphical user interfaces to automate steps previously done by humans or robotic process automation (RPA) 03:02:05
Preparing for AI Agent Integration
Every company should be actively working on evolving their digital assets for the use of AI agents, transforming them into tools for agent augmentation or generation 02:06:06.
Workflow Perspective: Automation vs. Augmentation
Technology can be used in two primary ways: automation and augmentation 04:12:35.
- Automation involves substituting a specific step with technology 04:19:20.
- Augmentation means performing a step quicker or better using technology 04:23:26.
While some tasks were previously automated or augmented with machine learning, the key difference with AI agents lies in their ability to tackle a broader set of cognitive tasks, operating on thoughts represented in natural language 05:04:07. AI agents can also act as a “glue” between tasks, composing them together without human intervention, which is only needed for feedback when necessary 05:20:00. This allows for “agentic workflows,” enabling the automation and delegation of multiple steps 05:36:39.
Challenges in Workflow Identification
A significant challenge with current AI implementation for building agents is identifying workflows and understanding their exact context 06:07:07. This context, including data, systems, transformations, and steps, is often undocumented and resides only in employees’ minds, or is distributed across teams and systems 06:15:17.
Network Perspective: Understanding Personas
Relying solely on traditional roles and job titles is insufficient for applying AI agents effectively 07:50:50. A deeper understanding requires a “network perspective,” defining employees by archetypes or personas based on their contributions to workflows 08:28:00. Examples of personas include:
- Silent Achiever: Low communication, high performance 08:57:59.
- Individual Contributor: Good communication and performance 09:04:06.
- Connector: Links non-adjacent teams 09:09:09.
- Multiplier: Enhances the work of other team members 09:16:00.
- Knowledge Hub: Provides domain expertise or necessary information 09:21:00.
Understanding these personas helps in projecting how AI agents might impact workflows and specific employees, enabling optimization for agent development 09:31:00. Identifying recurring patterns across different workflows, based on personas, can guide the development of specific agents for optimization 10:20:25.
Impact of AI Agents on Workflows and Roles
AI agents can drastically transform workflows and roles:
- A “silent achiever” with a coding assistant might become so productive they can handle work previously done by multiple people or steps 11:21:00.
- Coding assistants can make other team members more efficient 11:33:00.
- Agents can magnify the impact of a “multiplier” persona, extending their influence beyond adjacent team members to all team members, leading to higher performance and better communication 11:56:00.
- “Knowledge hub” personas are most prone to substitution by AI agents, as agents can dynamically support all team members with information, leading to overall efficiency 12:38:00.
These changes lead to several emerging patterns:
- Intelligence and domain knowledge become commodities: They become cheap and easily available, no longer a unique edge for employees or companies 15:32:00.
- Employees and companies must pivot towards multi-disciplinary know-how or deeper specialization 16:03:00.
- Engineers are increasingly working directly with product and domain experts, developing quicker with assistants and communicating faster thanks to domain assistant agents 16:19:00.
- Agents can act as “super connectors” 16:43:00.
- Context understanding gains more value over intelligence or domain expertise 16:48:00.
- AI “supercharges doers,” enabling people to perform more and quicker, potentially creating “100 times engineers” 17:04:00.
- Increased polarization in positions and tasks, requiring new specializations 17:27:00.
New roles in AI-mature organizations might emerge, such as “workflow miner” and “human AI orchestrator,” responsible for managing the interaction between humans and AI 17:42:00.
Democratizing AI Adoption
To fully accelerate the AI agent revolution, organizations must democratize the use of agents 27:05:00. This involves a phased approach:
- Access to general assistants: Providing common AI tools to all employees 18:30:00.
- Individual assistants: Giving employees personalized assistants with memory, adjustments, and access to personal databases for context-aware work 18:39:00.
- Employee-built agents: Enabling employees to quickly build agents using no-code, low-code, or coding tools. This requires cognitive self-awareness to translate mental steps into definable processes for agent creation 19:10:00.
- Digital twins: Creating AI agents representing experienced employees to retain organizational knowledge and expertise, acting as “time travelers” to reconstruct past insights and answer questions 20:24:00.
- Multi-employee agents: Agents serving entire teams or workflows 21:06:00.
- Swarm of agents: Deploying interconnected groups of agents 21:12:00.
This maturity in software stacks around LLMs and AI agents is making these advancements possible now 21:20:00.
Challenges and Opportunities in Adoption
While there are opportunities, there are significant challenges in AI adoption. Common challenges include groundedness, hallucinations, guardrails, and security 21:42:00. Two specific challenges highlighted are:
Ethics and Adoption Problem
- Adoption Problem: A key question is whether society is ready for AI to drive interactions and delegate subtasks to humans, rather than humans simply delegating tasks to AI 22:25:00. This is a significant cultural barrier that needs resolution 22:41:00.
- Ethics of Autonomy: As machines gain more freedom, they will require clearer moral standards 23:14:00. This brings up critical questions:
This necessitates increased engagement with morality, ethics, and philosophy to effectively operate AI technology 24:23:00.
Opportunities
Beyond the challenges, there are numerous opportunities for AI adoption, including:
- Simulating synthetic reality before real-life activities in marketing, labs, or other fields 24:53:00.
- Proactively identifying regulatory loopholes 25:06:00.
- Scouting for merger and acquisition (M&A) opportunities with AI agents 25:09:00.
Executive Summary
The widespread adoption of AI agents will lead to a rapid transformation of organizations and potentially drastic changes to organizational charts 27:19:00. A fundamental mindset shift is essential for adapting to new forms of interaction with technology, where AI may sometimes lead 27:31:00.
Key takeaways for executives:
- If an organization’s strategy still makes sense when “AI agent” is swapped with “machine learning” or “data,” it is likely outdated 26:29:00.
- AI agents offer new capabilities that create a significant difference in operations 26:56:00.
- A detailed understanding of workflows is crucial for maximizing benefits from agents 26:58:00.
- Democratizing agent creation and usage across the organization is key to accelerating the AI revolution 27:05:00.
- Understanding the “network of personas” (the human aspect) is vital for successful implementation 27:11:00.
- Ethics remain a crucial part of AI development and adoption 27:46:00.