Conversation is an interaction pattern in which two or more actors engage in a sustained, contextual exchange over multiple turns. It is the collaborative form of communication in agentic systems—a dialogue where each message builds on what came before, where meaning accumulates over time, and where the direction of the exchange emerges through the interaction itself. When a user and an agent work together through a troubleshooting session, asking clarifying questions and narrowing down the problem step by step, that’s conversation. When two agents negotiate a resource allocation, proposing and counter-proposing until they reach agreement, that’s conversation too.
Conversation is the most sophisticated of the four interaction primitives because it’s the only one that is inherently multi-turn and stateful. Delegation, retrieval, and notification are essentially atomic—a single message that carries a complete intent. Conversation is iterative. It requires memory, context tracking, and the ability to adapt based on what has already been said. This makes conversation the most powerful interaction pattern for complex, ambiguous, or exploratory work—and the most challenging to implement well.
How Conversation Works
A conversation unfolds through a sequence of turns—alternating exchanges between participants where each turn adds information, refines understanding, or advances the interaction toward some outcome. Unlike the other interaction patterns, a conversation doesn’t have a predetermined structure at the outset. The first turn might establish the topic. The second might clarify scope. The third might reveal an unexpected complication that changes the entire direction of the exchange. This emergent quality is what makes conversations so effective for problems that can’t be fully specified upfront.
Conversation depends on several underlying components working together. A thread provides the container for the exchange—the boundary that groups related turns into a coherent interaction and separates them from unrelated communication. Context accumulates across turns as information, decisions, and shared understanding build up over the course of the dialogue. Memory preserves relevant information from earlier in the conversation (and sometimes from prior conversations) so that participants can reference what was previously discussed without repeating themselves. And state tracks where the conversation currently stands—what has been resolved, what remains open, and what the next logical step might be.
In technical terms, conversation management is handled jointly by the platform and the language model. The platform manages thread infrastructure—storing conversation history, maintaining state, and handling the mechanics of multi-turn exchange. The LLM uses the conversation history in its context window to understand what has been discussed, what the current question is, and how to respond in a way that builds meaningfully on the prior exchange.
Conversation Between Humans and Agents
The most visible form of conversation in agentic systems is the dialogue between a human user and an agent. This is the interaction pattern that powers every chat interface, every virtual assistant, and every collaborative AI tool. A user describes a problem. The agent asks clarifying questions. The user provides additional detail. The agent proposes a solution. The user requests modifications. The agent refines its approach. Through this iterative exchange, the two participants converge on an outcome that neither could have fully specified at the start.
What makes human-agent conversation challenging is the asymmetry between participants. Human users communicate with natural language’s full ambiguity—incomplete sentences, implicit assumptions, emotional undertones, references to shared context that may or may not exist. Agents need to navigate this ambiguity gracefully, asking for clarification when needed without being annoying, making reasonable assumptions when appropriate without being presumptuous, and maintaining a consistent thread of understanding even when the human’s communication style is inconsistent.
The best human-agent conversations feel like working with a capable colleague. The agent remembers what was discussed earlier, doesn’t ask questions that have already been answered, picks up on contextual cues, and adds genuine value rather than simply reflecting the user’s words back at them. Achieving this requires not just good language models but good conversation design—thoughtful decisions about how much history to retain, when to summarize versus preserve verbatim, and how to handle conversations that span hours, days, or weeks.
Conversation Between Agents
Agent-to-agent conversation is less visible but increasingly important in multi-agent systems. When agents need to coordinate on tasks that can’t be fully specified through a single delegation message—because the work is ambiguous, because it requires negotiation, or because the right approach depends on information that emerges during the interaction—they engage in conversation.
Consider two agents responsible for different aspects of an incident response. The monitoring agent has detected an anomaly but isn’t sure whether it’s a real incident or a false positive. The diagnostic agent has access to system logs and historical patterns. Through a multi-turn conversation, the monitoring agent describes what it observed, the diagnostic agent asks for specific metrics, the monitoring agent provides them, the diagnostic agent compares against historical baselines, and together they reach a determination. This kind of collaborative reasoning through dialogue is something that a single delegation message can’t capture—it requires the back-and-forth of conversation.
Agent-to-agent conversation also appears in negotiation scenarios. When an orchestrator needs to allocate limited resources among competing tasks, it might engage specialist agents in a conversation about priorities, trade-offs, and acceptable compromises. “Can you complete this analysis with a smaller dataset?” “Yes, but the confidence interval would widen from 5% to 12%.” “Is that acceptable for the decision at hand?” This kind of structured negotiation is fundamentally conversational—it requires iterative exchange where each turn depends on the content of the previous one.
Conversation vs. Other Interaction Patterns
Conversation is distinguished from the other three interaction primitives by its multi-turn, stateful nature.
Conversation says “let’s work through this together.” It is collaborative, iterative, and builds understanding over time.
Delegation says “do this.” It’s a single directive that expects execution—not an ongoing dialogue. An orchestrator that delegates a task doesn’t expect to have a conversation about it (though it might need to if the task turns out to be ambiguous).
Retrieval says “tell me this.” It’s a single question that expects a single answer. The exchange is complete once the information is provided.
Notification says “this happened.” It’s a one-way broadcast with no expectation of response at all, let alone multi-turn exchange.
In practice, conversations often contain the other interaction patterns within them. A single conversation might include retrieval turns (“what’s the current status?”), delegation turns (“go ahead and process the refund”), and notification-like turns (“just so you know, the customer has filed a formal complaint”). The conversation provides the contextual container; the individual turns carry the specific interaction patterns.
Context Management: The Central Challenge
The defining technical challenge of conversation is context management—maintaining a coherent, useful representation of what has been discussed across an exchange that may span dozens or hundreds of turns.
Language models have finite context windows. A conversation that has been running for fifty turns contains far more history than most models can process simultaneously. This creates a fundamental design tension: including more history gives the agent better context for understanding the current turn, but it also consumes context window space that could be used for other information (instructions, tool results, retrieved knowledge) and increases latency and cost.
Effective conversation design addresses this tension through several strategies. Summarization compresses older parts of the conversation into concise summaries that preserve key decisions and facts while discarding the specific back-and-forth that produced them. Selective retention keeps the most recent turns in full detail while summarizing or dropping earlier turns, reflecting the cognitive reality that recent context is usually more relevant than distant history. Semantic indexing stores the full conversation history externally and retrieves relevant portions when the current turn references earlier topics, turning conversation memory into a retrieval problem.
None of these strategies is perfect. Summarization loses nuance. Selective retention loses context from early in the conversation that might be relevant later. Semantic indexing adds latency and complexity. The right approach depends on the nature of the conversation—a quick troubleshooting session might only need the last five turns, while a complex project planning conversation might need careful summarization of everything discussed over multiple sessions.
Conversation and Memory
Conversation has a natural relationship with memory systems. While conversation context spans a single interaction, memory extends context across interactions—allowing an agent to remember what was discussed in previous conversations and bring that knowledge into the current one.
A customer calling support for the third time about the same issue doesn’t want to re-explain everything from scratch. A project manager continuing a planning session from last week expects the agent to remember what was decided. Memory transforms isolated conversations into ongoing relationships, where context accumulates not just across turns within a single exchange but across exchanges over time.
This distinction between conversation-level context and cross-conversation memory is architecturally significant. Conversation context is managed within the thread—it’s the responsibility of the conversation management infrastructure. Cross-conversation memory is managed by memory systems—knowledge tools that store and retrieve information from prior interactions. Both contribute to the agent’s ability to hold coherent, contextual dialogue, but they operate at different time scales and require different infrastructure.
Why Conversation Matters in Enterprise Contexts
In enterprise environments, conversation is the interaction pattern that handles complexity and ambiguity—the situations where a simple command-and-response won’t suffice. Complex customer issues, nuanced business decisions, exploratory analysis, collaborative planning—these are all domains where the iterative, context-building nature of conversation is essential.
Conversation also provides something the other interaction patterns don’t: a natural audit trail of reasoning. When an agent and a user work through a problem via conversation, the thread itself documents the information that was considered, the questions that were asked, the alternatives that were explored, and the reasoning that led to the final decision. This is tremendously valuable for compliance, quality assurance, and continuous improvement—far more valuable than a simple log entry that records an input and an output with no visibility into what happened in between.
For multi-agent systems, conversation enables a form of distributed reasoning that can’t be achieved through delegation alone. When the right answer depends on synthesizing perspectives from multiple specialist agents, structured conversation—where agents exchange views, challenge assumptions, and build on each other’s analysis—produces richer outcomes than any single agent could achieve independently.
Also Known As
Conversation appears under various names depending on the platform and context. You’ll encounter it as dialogue (in conversational AI literature), chat sessions or threads (in messaging and platform contexts), multi-turn interactions (in LLM-specific contexts), discourse (in formal communication theory), or collaborative exchanges (in multi-agent frameworks). The underlying infrastructure is sometimes called conversation management, session management, or dialogue management. In agent-to-agent contexts, sustained multi-turn exchanges are sometimes called negotiations, deliberations, or collaborative reasoning sessions. The defining characteristic across all terminology is the same: a sustained, contextual, multi-turn exchange where meaning builds incrementally through iterative dialogue.
Key Takeaways
Conversation is the collaborative, multi-turn interaction pattern that enables agents to handle complex, ambiguous, and exploratory work. It is the only interaction primitive that is inherently stateful—requiring memory, context management, and the ability to build understanding incrementally across turns. In the agentic primitives framework, conversation sits alongside delegation, retrieval, and notification as the four patterns that define how actors communicate, and it is the pattern most directly tied to handling the kind of nuanced, evolving situations that simple command-and-response interactions can’t address. Getting conversation design right—particularly context management and memory integration—is one of the most impactful investments in agent quality.