Day 50 of 100 Days Agentic Engineer Challenge: Redefining AI Agents

6 min read1 day ago

I always thought an AI agent was like a fully autonomous software — something that would work for a week to reach a goal, then learn and improve with every iteration. But after watching a webinar yesterday, I realized it’s often presented as software that can actually take on specific job roles in a company. For example, an AI agent could handle tasks like setting up social media ads based on a marketing director’s specs or writing 10 articles a day for a content marketer. It makes sense, but doesn’t that sound more like an assistant than an agent?

I’m just curious why different LLM frameworks have started switching the name from “assistant” to “agent,” even though they’re offering pretty much the same functionality. And tools like Langchain or n8n used to have a block called “assistant” with the ability to add tools and functions. Now, it’s labeled “agent” with the same features. Is this just a trend or marketing move?

Today, I asked Perplexity Deep Research to answer this question — below is the report. But before we dive into that, let’s review my daily tasks routine.

Daily Tasks Routine

💪 Physical Activity — I did 70 squats.
😴 Seven Hours of Sleep — I slept for 7 hours.
🤖 AI Agent — I’m building a true AI agent.
⏳ PAIC — In queue.
📊 Data Science — In queue.

If you want to know what all these tasks are about, read the introduction to the 100 Days Agentic Engineer Challenge.

Redefining AI Agents: Beyond Autonomy to Purpose-Driven Intelligence

Recent discussions in artificial intelligence have sparked a debate that cuts to the core of how we conceptualize intelligent systems: What truly defines an AI agent, and how does it differ from the AI assistants we’ve grown accustomed to? This question gained urgency after a 2025 webinar challenged conventional wisdom by presenting AI agents as digital employees capable of assuming specific job roles — from social media campaign managers to high-volume content creators — while industry frameworks like LangChain and n8n rebranded their “assistant” modules as “agents” without apparent functional changes. To unravel this, we must re-examine the agent paradigm through three lenses: technical autonomy, task ownership, and the evolving semantics of AI tooling.

The Myth of Universal Autonomy: A New Framework for Defining Agents

The classical definition of AI agents — systems that operate autonomously for extended periods, self-improving through iterative learning — has dominated academic discourse since the 1990s (8 10). However, real-world implementations reveal a spectrum of autonomy better measured by a system’s capacity for purpose-driven decision-making rather than temporal independence.

1. Goal-Oriented Agency vs. Task Completion

An AI assistant excels at discrete tasks: composing emails, generating reports, or answering FAQs when prompted (1 7). Its actions are reactive, bounded by explicit user instructions. In contrast, an AI agent owns outcomes rather than tasks. Consider a social media manager agent:

Assistant Approach: “Create 5 Facebook ads targeting millennials based on Q2 demographics” → Executes the request verbatim.
Agent Approach: “Increase millennial engagement on Facebook by 20% this quarter” → Autonomously analyzes performance data, A/B tests ad variants, reallocates budgets between platforms, and adjusts targeting parameters without step-by-step guidance (6 14).

This distinction aligns with IBM’s characterization: agents exhibit “multicomponent autonomy,” dynamically breaking goals into subtasks while leveraging external tools and data streams8. The webinar’s example of role-based agents fits this model — when given a marketing objective, the system assumes responsibility for the entire campaign lifecycle, not individual creative tasks (15).

The Job Role Paradox: When Assistants Become Agents

The confusion between assistants and agents often stems from overlapping tooling capabilities. Modern frameworks like AutoGen and LangChain allow both archetypes to access similar functions — database queries, API integrations, code execution (2 12). The differentiation emerges in how these tools get used:

Table 1: Functional differences between assistants and agents, adapted from (1 8 10)

A content generation “agent” that produces 10 articles daily might initially resemble an assistant. However, if it autonomously curates topics based on trending SEO data, adjusts writing style per platform analytics, and reallocates output volume between blogs and LinkedIn based on engagement metrics, it crosses into agent territory (6 11). The key is persistent environmental interaction beyond a single task thread.

Semantic Shift or Substance? Decoding the Framework Rebranding

The migration from “assistant” to “agent” in tools like n8n and LangChain reflects deeper architectural evolution rather than mere marketing hype. Earlier implementations focused on task chaining — linear sequences of API calls triggered by user input (12 13). The new agent-centric models prioritize:

Dynamic Tool Selection: Agents analyze problems to choose appropriate tools from their arsenal, whereas assistants follow predefined workflows (9).

# LangChain Agent vs. Assistant workflow comparison[12]  
# Assistant approach (fixed pipeline)  
assistant = OpenAIAssistantRunnable.create_assistant(tools=[tool1, tool2])  
response = assistant.invoke("Generate report using tool1 then tool2")  

# Agent approach (contextual tool choice)  
agent = OpenAIAssistantRunnable.create_agent(tools=[tool1, tool2, tool3])  
response = agent.invoke("Optimize Q3 sales strategy")  # Agent selects optimal tools

2. Stateful Execution: Agents maintain session-aware contexts, allowing multi-step reasoning across interactions. The n8n documentation notes agents “run multiple times” per query, evaluating intermediate outputs before finalizing responses (9).

3. Role-Based Permissions: Enterprise platforms now tie agents to organizational roles (e.g., “Social Media Manager”) with defined authority limits, whereas assistants typically lack such governance (4 15).

This architectural shift enables systems like Anthrologic’s advertising agent to autonomously adjust campaign budgets across platforms while adhering to CMO-defined guardrails — a leap beyond simple automation (3 14).

The Hype Factor: Separating Substance from Semantic Games

While legitimate technical advancements underpin many “agent” rebrandings, market forces have diluted the term’s precision. Three factors drive this:

Vendor Differentiation: With ChatGPT and Copilot dominating the assistant space, startups position “agents” as premium offerings — despite overlapping features (3 11).
Expectation Management: Calling a chatbot an “agent” primes users to tolerate higher latency (“It’s thinking!”) versus real-time assistants (7).
Investment Narratives: Venture capital increasingly funds “agentic AI” startups, creating incentive to relabel existing tools (3 15).

However, substantive innovations exist. Microsoft’s AutoGen framework demonstrates true agency through conversational agents that collaborate, debate solutions, and self-correct — a paradigm beyond single-assistant capabilities(2). Similarly, Anthrologic’s advertising agents incorporate market simulations to predict campaign outcomes, exhibiting emergent strategic planning (3).

Conclusion: Agency as a Spectrum, Not a Binary

The agent vs. assistant debate obscures a critical insight: autonomy exists on a continuum. Rather than fixating on labels, organizations should evaluate systems through these questions:

Does it make context-aware decisions beyond its initial prompt?
Can it reorient goals based on new data without human intervention?
Does it coordinate multiple tools/services adaptively?

A social media manager that schedules posts is an assistant; one that negotiates ad placements with influencer agents while balancing CAC and CPM metrics qualifies as an agent (14 15). As frameworks mature, the distinction will increasingly hinge on strategic capacity rather than semantic branding. What remains clear is that the age of passive AI tools is ending — the future belongs to systems that don’t just execute tasks, but own outcomes.

The author is an AI systems architect specializing in agentic workflow design. Follow for analysis on emergent AI paradigms.

I really enjoyed how Perplexity described the author! :) As for today, I’m not updating the status of my agent, as I wasn’t able to make any progress on the technical side of the project I’m working on.