How AI tools are evolving beyond simple prompts to enable fluid, multi-turn collaboration

The Interaction Model in Action

Scenario 1: A developer joins a new React project and types “Add authentication to this app.” The AI analyzes the codebase, identifies the current architecture, implements JWT authentication across multiple files, and creates middleware. Mid-process, they interrupt: “Actually, use OAuth instead.” The AI stops, acknowledges the change, and refactors the entire authentication system. No mode switching, no starting over—just conversational steering of autonomous execution.

Scenario 2: A content creator working in Claude Artifacts says “Create a data visualization dashboard.” The AI builds an interactive component with sample data. They continue: “Make it responsive and add filtering options.” The AI iterates in real-time, maintaining the conversation context while executing complex modifications.

Scenario 3: A customer contacts support saying “I was charged twice for my subscription.” The AI agent converses to understand the issue, autonomously researches billing history, identifies the duplicate charge, processes a refund, and returns to confirm resolution—all within the same conversation thread.

These scenarios represent a shift in human-AI collaboration, currently defined using fragmented terminology like agent mode, agentic editing, agentic coding, “interactive AI,” and “conversational AI” across different tools, but lacking consistent terminology.

Understanding Conversational Agentic Systems

The Technology Evolution

The AI industry developed along two parallel paths that are now converging:

• Early Chatbots (1960s-1990s): Basic pattern matching and decision trees, starting with ELIZA (1966) and early rule-based systems
• Modern Chatbots (2000s-2010s): Intent-driven bots with improved natural language processing and enterprise integration
• Conversational AI (2010s+): Advanced dialogue systems with context awareness and machine learning capabilities ¹
• Generative AI (2022+): LLMs like ChatGPT enabling rich text generation and complex reasoning
• Agentic AI (2023+): Integration of LLM capabilities with autonomous reasoning and tool usage ²
• Conversational Agentic Systems (2024+): Seamless integration where conversation and autonomous action become unified

Historically, conversational AI focused on natural language understanding, while agentic AI focused on autonomous decision-making and action. Most implementations still integrate these as separate layers: conversational AI as the interface, agentic AI as the execution engine.

What’s emerging is a category that seamlessly integrates both capabilities. These require distinguishing between two complementary concepts:

Core Definitions

Conversational Agentic Systems are the technical architecture that enables AI agents to execute complex multi-step tasks while maintaining natural language interfaces throughout the process. These systems combine conversational AI and agentic AI so seamlessly that the distinction becomes invisible to users.

Conversational Agentic Workflows are the interaction patterns these systems enable—where humans and AI agents dynamically exchange control within unified conversation threads, with AI handling autonomous execution while remaining conversationally steerable by humans.

This architectural approach aligns with Anthropic’s recent guidance on building effective agents, which emphasizes that the most successful implementations use “simple, composable patterns rather than complex frameworks” and distinguishes between workflows (predefined code paths) and agents (systems where LLMs dynamically direct their own processes).

The Concept Hierarchy

As tools like Zed introduce “agentic editing” and the development community embraces “vibe coding,” it’s useful to understand how these concepts relate:

Vibe Coding = Human methodology (how developers choose to work informally and intuitively) ³

Agentic Editing = AI capability (autonomous code editing within development tools) ⁴

Conversational Agentic Workflows = Interaction patterns (dynamic human-AI collaboration within conversation)

Conversational Agentic Systems = Foundational concept (the platforms that enable these workflows across all domains)

Why This is the Foundation

Broader Scope: While agentic editing focuses on code, conversational agentic systems explain the same pattern emerging in content creation, business analysis, customer service, and beyond. You can implement agentic editing within a conversational agentic system, but these concepts enable applications far beyond code editing.

Technical Precision: “Conversational” captures the key differentiator that “agentic editing” misses—the interface mechanism and dynamic control-switching that makes this collaboration possible.

Cross-Domain Applicability: Conversational agentic systems offer the common language that explains why Zed’s agentic editing works, why Claude’s dynamic content creation feels natural, and why similar patterns are emerging across industries.

What Makes This Different: Conversational Steerability

What makes this different isn’t that humans and AI work together—that’s existed for years. What’s different is that AI can execute complex multi-step processes while remaining conversationally steerable throughout:

• AI maintains execution control during analysis, solution generation, and implementation tasks
• Humans provide conversational steering through natural language interruptions and redirections
• No mode switching required - interruption capabilities vary by system design and user role
• Context persists across all interactions, enabling fluid collaboration

Unlike traditional collaboration models, conversational agentic workflows eliminate the artificial boundary between “thinking” and “doing”—conversation seamlessly incorporates both planning and execution.

The approach works, but the industry lacks shared vocabulary. Major AI companies are already building these systems—they just call them different things. Each vendor independently arrived at similar solutions—conversational interfaces that can trigger and steer autonomous execution—showing this is an effective approach to human-AI collaboration.

Visualizing the Workflow Pattern

This shows the conversational agentic workflow pattern: AI-driven execution with conversational human steering within a single conversation thread, where humans can interrupt and redirect AI execution at any point through natural language.

Why This Approach Works

Traditional approaches to human-AI collaboration create fundamental constraints that limit their effectiveness:

Instead of forcing a choice between human control or AI automation, conversational agentic systems enable AI-driven execution that remains conversationally steerable throughout the entire process. Control flows naturally based on task requirements and human input, creating a fundamentally more flexible collaboration model.

Dynamic Control Flow

These systems share four core design principles:

• 💬 Conversation as Primary Interface: All interaction happens through natural language, eliminating mode switching and interface complexity
• 🤖 Autonomous Execution: AI can take multi-step actions, planning and executing complex workflows beyond simple responses
• ⚡ Continuous Steerability: Change direction mid-process through conversation feedback without stopping execution
• 🧠 Persistent Context: Rich memory across entire collaboration builds knowledge and decisions over time

How These Systems Work in Practice

This workflow pattern appears across development tools and broader industries, with major AI companies building these systems—they just call them different things:

Development Tools

Pattern Examples:

• Claude Code: Natural language → autonomous development workflows → conversational refinement
• Google’s Jules: Conversation → autonomous coding plans → git workflow integration
• Zed’s agentic editing: Conversation → multi-file code changes → contextual adaptation
• Cursor’s agent mode: Dialogue → codebase analysis → execution → steering

Customer Service

Example: Zendesk’s AI agents that start with conversational triage, autonomously research account history and billing systems, execute corrective actions (process refunds, update accounts, escalate to specialists), then return to natural conversation to confirm resolution. Similarly, Mastercard utilizes AI agents to analyze transaction patterns in real-time, block suspicious activities, and adapt to new fraud techniques.

Workflow Pattern: Conversation → Autonomous analysis → Autonomous action → Conversational confirmation

App Development

Example: Platforms like Replit, Lovable, and Bolt exemplify conversational agentic systems for software creation. You describe what you want to build through natural conversation, the AI analyzes requirements, autonomously generates complete applications with databases and deployment, then iterates based on conversational feedback.

Workflow Pattern: Conversational requirements → Autonomous development → Live deployment → Conversational iteration

Content Creation

Example: Claude Artifacts where you brainstorm through conversation, AI autonomously builds and renders content, you provide feedback conversationally, AI iterates and refines—all within a single thread.

Workflow Pattern: Conversational ideation → Autonomous creation → Conversational refinement → Autonomous iteration

Business Intelligence

Example: AI systems like Microsoft Copilot for Power BI that converse to understand business questions, autonomously query databases and process datasets, generate insights and visualizations, then present findings conversationally for business stakeholder refinement.

Workflow Pattern: Conversational requirements gathering → Autonomous analysis → Conversational insight delivery → Autonomous refinement

Research and Analysis

Example: AI research assistants like Perplexity that discuss your research goals, autonomously gather information across multiple sources, synthesize findings, and engage in conversational back-and-forth to refine conclusions.

Workflow Pattern: Conversational goal setting → Autonomous research → Conversational synthesis → Autonomous validation

Retail and E-commerce

Example: Walmart is redesigning its systems to allow third-party shopping agents to query prices and place orders autonomously on behalf of consumers. Amazon’s pricing algorithms operate as autonomous agents that continuously monitor competitor pricing and adjust prices across millions of products in real-time.

Workflow Pattern: Conversational intent → Autonomous market analysis → Autonomous action → Conversational confirmation

The Common Pattern: Conversation naturally incorporates autonomous action, with AI handling execution while remaining conversationally steerable throughout the process.

Recognizing Conversational Agentic Workflows

How do you know when you’re experiencing a conversational agentic workflow? Apply these recognition tests:

The Conversation Test

Can you accomplish complex, multi-step workflows through natural language alone, without switching to separate interfaces or “command modes”?

Quick Test: If you need to click buttons, switch tabs, or enter a different mode to complete your workflow, it’s not a conversational agentic workflow. Everything should flow through dialogue.

The Agency Test

Can the AI take autonomous action beyond simple responses—planning multi-step processes, executing tasks, accessing tools, and adapting its approach based on results?

Quick Test: Ask the AI to “implement user authentication.” If it only offers code suggestions, it’s assistive. If it can analyze your codebase, plan the implementation, execute across multiple files, and notify you when complete, it’s agentic.

The Flow Test

Can you influence AI execution through natural conversation, either by interrupting mid-process or redirecting between steps?

Quick Test: Notice how interruption works during execution. In conversational agentic systems like Cursor and Zed, you can explicitly stop AI execution mid-process and redirect. Zed provides notifications when background agents complete their work, while Cursor runs agents in cloud environments with GitHub integration. In customer service scenarios, the AI typically completes its autonomous workflow (research, actions) before returning to conversation—interruption capabilities vary by use case and user role.

The Context Test

Does the conversation maintain rich context across all interactions, with both parties naturally referencing earlier exchanges and building on previous decisions?

Quick Test: Reference something from earlier in the conversation. If the AI understands and incorporates that context into new actions, it’s maintaining conversational context effectively.

If a tool exhibits all four characteristics, you’re experiencing a conversational agentic workflow.

What Developers Are Saying

The developer community’s response to agentic editing capabilities has been positive, as evidenced by feedback collected on Zed’s agentic features page:

“Zed has gone leaps and bounds on the AI front recently—stopped using cursor altogether and it feels so good to be free from an Electron editor.”
— Ben (@suppyben)

“The right IDE + the right AI agent = productivity I couldn’t have imagined a year ago. Zed and Claude Code in the terminal has been my winning combo.”
— Adrian Muntean (@agmuntean)

“Killing it! I’ve been using the new agent mode and it is great and polished as ever.”
— mick (@mickcodez)

“I think @zeddotdev might legit be the end game code editor.”
— Jack Smith (@jacksmithdotxyz)

“The new @zeddotdev agent experience is ✨ also love that it sends you a notification when done, actually kind of weird that e.g. cursor doesn’t do that?”
— Martin Klepsch (@martinklepsch)

These testimonials suggest developers are experiencing a different way of working where the boundary between human intention and AI execution becomes less distinct.

Practical Implications

What This Means in Practice

For Teams: Teams can tackle bigger problems with the same people. AI handles the analysis and implementation grunt work while humans focus on strategy, judgment, and creative problem-solving.

For Building Things: The gap between having an idea and testing it shrinks dramatically when you can prototype and iterate through conversation.

For How We Work: Instead of debating humans versus AI, we’re moving toward humans and AI working together through conversation—each doing what they’re best at.

This isn’t just better tools—it’s a fundamentally different way of working where the boundary between thinking and doing becomes fluid. Agentic editing in code editors is just the beginning.

Why This Language Helps

For Product Teams: Learn from what’s already working in Cursor, Zed, and Claude Code. The successful pattern: workflow-centric design, progressive capability disclosure, and natural interruption mechanisms.

For Evaluating Tools: Ask whether a tool implements conversational agentic workflows. If it only offers suggestions or requires mode switching, it’s incremental. If it enables conversational steering of autonomous execution, it’s potentially transformative.

For Developers: The technical requirements are clear: conversational context preservation, interruptible execution, tool integration, and workflow orchestration.

What examples of conversational agentic workflows have you encountered in your work? How do you see these patterns evolving in your industry?

Disclosure: This article was developed through a conversational agentic workflow using Cursor with Claude Sonnet 4 as a collaborative partner for research, analysis, and editorial refinement—demonstrating the very concepts discussed within.