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    <id>https://parlant.io/blog</id>
    <title>Parlant Blog</title>
    <updated>2026-04-25T00:00:00.000Z</updated>
    <generator>https://github.com/jpmonette/feed</generator>
    <link rel="alternate" href="https://parlant.io/blog"/>
    <subtitle>Parlant Blog</subtitle>
    <icon>https://parlant.io/logo/icons/mstile-150x150.png</icon>
    <entry>
        <title type="html"><![CDATA[The Age of Harnesses]]></title>
        <id>https://parlant.io/blog/the-age-of-harnesses</id>
        <link href="https://parlant.io/blog/the-age-of-harnesses"/>
        <updated>2026-04-25T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[What is a harness, and why has it become the dominant variable in applied LLM capability? An attempt at a working definition — and what it means for the agents that aren't Claude Code.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="harness" term="harness"/>
        <category label="context-engineering" term="context-engineering"/>
        <category label="claude-code" term="claude-code"/>
        <category label="conversational-ai" term="conversational-ai"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant 3.3: Scaling Control]]></title>
        <id>https://parlant.io/blog/parlant-3-3-release</id>
        <link href="https://parlant.io/blog/parlant-3-3-release"/>
        <updated>2026-03-15T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Parlant 3.3 introduces tag-based relationships, numeric guideline priorities, transient guidelines, Agent.utter(), and easier metadata management from tools.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="release" term="release"/>
        <category label="tags" term="tags"/>
        <category label="relationships" term="relationships"/>
        <category label="transient-guidelines" term="transient-guidelines"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant 3.2: Streaming Responses]]></title>
        <id>https://parlant.io/blog/parlant-3-2-streaming-responses</id>
        <link href="https://parlant.io/blog/parlant-3-2-streaming-responses"/>
        <updated>2026-02-08T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Parlant 3.2 introduces streaming message output, entity labels with session propagation, and improvements across guidelines, journeys, and canned responses.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="release" term="release"/>
        <category label="streaming" term="streaming"/>
        <category label="labels" term="labels"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant 3.1]]></title>
        <id>https://parlant.io/blog/parlant-3-1-release</id>
        <link href="https://parlant.io/blog/parlant-3-1-release"/>
        <updated>2026-01-01T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Parlant 3.1 introduces Emcie for cost-optimized inference, and substantial improvements across guidelines, tools, canned responses, and observability.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="release" term="release"/>
        <category label="control" term="control"/>
        <category label="scale" term="scale"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Criticality-Based Resource Allocation]]></title>
        <id>https://parlant.io/blog/criticality-in-customer-facing-agents</id>
        <link href="https://parlant.io/blog/criticality-in-customer-facing-agents"/>
        <updated>2025-12-23T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Not all instructions carry the same weight. Learn how criticality levels let you control the accuracy-cost tradeoff in conversational AI agents.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="criticality" term="criticality"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="guidelines" term="guidelines"/>
        <category label="tools" term="tools"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Inside Parlant's Guideline Matching Engine]]></title>
        <id>https://parlant.io/blog/inside-parlant-guideline-matching-engine</id>
        <link href="https://parlant.io/blog/inside-parlant-guideline-matching-engine"/>
        <updated>2025-11-01T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[When we started building guideline matching, we thought semantic similarity would be enough. We were wrong. Here's what it actually takes to make AI agents reliably follow hundreds of business rules.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="guideline-matching" term="guideline-matching"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="arqs" term="arqs"/>
        <category label="llm-alignment" term="llm-alignment"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant vs LangGraph]]></title>
        <id>https://parlant.io/blog/parlant-vs-langgraph</id>
        <link href="https://parlant.io/blog/parlant-vs-langgraph"/>
        <updated>2025-10-18T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[LangGraph's router and multi-agent patterns are powerful for orchestration, but they create inherent challenges for free-form conversational AI. Here's why, and how Parlant solves it differently.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="langgraph" term="langgraph"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="multi-agent-systems" term="multi-agent-systems"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant vs DSPy]]></title>
        <id>https://parlant.io/blog/parlant-vs-dspy</id>
        <link href="https://parlant.io/blog/parlant-vs-dspy"/>
        <updated>2025-10-01T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Many people are asking if Parlant is like DSPy. Let's clear up the confusion and explain what each framework actually does.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="dspy" term="dspy"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="alignment" term="alignment"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[How Parlant Guarantees Compliance]]></title>
        <id>https://parlant.io/blog/how-parlant-guarantees-compliance</id>
        <link href="https://parlant.io/blog/how-parlant-guarantees-compliance"/>
        <updated>2025-08-17T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[In this article, we explore the innovative strategies Parlant employs to ensure compliance and mitigate risks associated with LLMs.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="release" term="release"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="production" term="production"/>
        <category label="performance" term="performance"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Parlant 3.0 — Reliable AI Agents]]></title>
        <id>https://parlant.io/blog/parlant-3-0-release</id>
        <link href="https://parlant.io/blog/parlant-3-0-release"/>
        <updated>2025-08-15T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Introducing Parlant 3.0 with massive performance improvements, enhanced journeys, canned responses, and comprehensive production features for enterprise deployment.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="parlant" term="parlant"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="release" term="release"/>
        <category label="conversational-ai" term="conversational-ai"/>
        <category label="production" term="production"/>
        <category label="performance" term="performance"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Agentic Backends: What I Wish Someone Had Told Me About API Design for LLMs]]></title>
        <id>https://parlant.io/blog/what-no-one-tells-you-about-agentic-api-design</id>
        <link href="https://parlant.io/blog/what-no-one-tells-you-about-agentic-api-design"/>
        <updated>2025-07-15T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[A deep dive into the challenges and solutions of designing APIs for agentic applications.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="ai-agents" term="ai-agents"/>
        <category label="embedding" term="embedding"/>
        <category label="retrieval" term="retrieval"/>
        <category label="vector-database" term="vector-database"/>
        <category label="vector-search" term="vector-search"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Increasing the Accuracy of Embedding-Based Retrieval]]></title>
        <id>https://parlant.io/blog/increasing-accuracy-of-embedding-based-retrieval</id>
        <link href="https://parlant.io/blog/increasing-accuracy-of-embedding-based-retrieval"/>
        <updated>2025-07-08T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Explore the innovative techniques and strategies we've implemented to enhance the accuracy of embedding-based retrieval systems.]]></summary>
        <author>
            <name>Dor Zohar</name>
            <uri>https://www.linkedin.com/in/dor-zohar-6b803895/</uri>
        </author>
        <category label="ai-agents" term="ai-agents"/>
        <category label="embedding" term="embedding"/>
        <category label="retrieval" term="retrieval"/>
        <category label="vector-database" term="vector-database"/>
        <category label="vector-search" term="vector-search"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Why We Built Parlant: From Mission-Critical Systems to Conversational AI]]></title>
        <id>https://parlant.io/blog/why-we-built-parlant</id>
        <link href="https://parlant.io/blog/why-we-built-parlant"/>
        <updated>2025-06-29T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Discover how our team's journey from building mission-critical systems led to the creation of Parlant, an open-source Conversational AI engine designed for reliable customer interactions.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="conversation-modeling" term="conversation-modeling"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="customer-facing-ai" term="customer-facing-ai"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Building Customer-Facing AI Agents in 2025: From the  "How" to the "What" and Back Again]]></title>
        <id>https://parlant.io/blog/ai-agents-from-how-to-what-and-back-again</id>
        <link href="https://parlant.io/blog/ai-agents-from-how-to-what-and-back-again"/>
        <updated>2025-06-25T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[In 2025, the most effective way to build customer-facing AI agents combines the adaptability of LLMs with the structure and precision of traditional conversation design.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="conversation-modeling" term="conversation-modeling"/>
        <category label="ai-agents" term="ai-agents"/>
        <category label="customer-facing-ai" term="customer-facing-ai"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Solving LLM Hallucinations in Conversational, Customer-Facing Use Cases]]></title>
        <id>https://parlant.io/blog/solving-llm-hallucinations-in-conversational-customer-facing-use-cases</id>
        <link href="https://parlant.io/blog/solving-llm-hallucinations-in-conversational-customer-facing-use-cases"/>
        <updated>2025-06-18T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[The challenges of LLM hallucinations in large-scale conversational AI and strategies for mitigating them.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="conversation-modeling" term="conversation-modeling"/>
        <category label="hallucinations" term="hallucinations"/>
        <category label="utterance-templates" term="utterance-templates"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[From ELIZA to Parlant: The Evolution of Conversational AI Systems and Paradigms]]></title>
        <id>https://parlant.io/blog/evolution-of-conversational-ai</id>
        <link href="https://parlant.io/blog/evolution-of-conversational-ai"/>
        <updated>2025-05-01T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[Conversational AI has evolved dramatically over decades. From rule-based chatbots like ELIZA in the 1960s, through  hybrid ML+rule frameworks, and, recently, LLMs. Now, state-of-the-art Conversation Modeling engines marry the generative power of LLMs with structured guidelines, allowing richer interactions that remain controllable, easier to develop iteratively, and more scalable to test in real-world scenarios.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="llm" term="llm"/>
        <category label="machine-learning" term="machine-learning"/>
        <category label="conversation-modeling" term="conversation-modeling"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Why Generic RAG Frameworks Can't Catch On]]></title>
        <id>https://parlant.io/blog/generic-rag-will-never-catch-on</id>
        <link href="https://parlant.io/blog/generic-rag-will-never-catch-on"/>
        <updated>2025-04-24T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[In the market for generic RAG frameworks, the different providers are fighting over who can provide 67% accuracy versus 65%. And when you run an off-the-shelf RAG framework on your use case, it will end up closer to 50% accuracy. Is this the best that the industry can do?]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="rag" term="rag"/>
        <category label="vectordb" term="vectordb"/>
        <category label="llm" term="llm"/>
        <category label="machine-learning" term="machine-learning"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Are Autoregressive LLMs Really Doomed? (A Commentary Upon Yann LeCun's Recent Key Note)]]></title>
        <id>https://parlant.io/blog/are-autoregressive-llms-really-doomed</id>
        <link href="https://parlant.io/blog/are-autoregressive-llms-really-doomed"/>
        <updated>2025-02-09T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[A commentary upon Yann LeCun's key note at AI Action Summit, along with some supplementary explanations on how LLMs work under the hood]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="llm" term="llm"/>
        <category label="machine-learning" term="machine-learning"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[What Is Autoregression in LLMs?]]></title>
        <id>https://parlant.io/blog/what-is-autoregression</id>
        <link href="https://parlant.io/blog/what-is-autoregression"/>
        <updated>2025-02-09T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[A peek under the hood into how LLMs work]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="llm" term="llm"/>
        <category label="machine-learning" term="machine-learning"/>
    </entry>
    <entry>
        <title type="html"><![CDATA[Rethinking How We Build Customer-Facing AI Agents]]></title>
        <id>https://parlant.io/blog/rethinking-how-we-build-customer-facing-ai-agents</id>
        <link href="https://parlant.io/blog/rethinking-how-we-build-customer-facing-ai-agents"/>
        <updated>2024-12-09T00:00:00.000Z</updated>
        <summary type="html"><![CDATA[A deep dive into today's prevalent methodologies, the challenges that come with each of them, and where the future may lie.]]></summary>
        <author>
            <name>Yam Marcovitz</name>
            <uri>https://www.linkedin.com/in/yam-marcovic/</uri>
        </author>
        <category label="methodologies" term="methodologies"/>
    </entry>
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