Context Engineering for Chat Agents
Parlant offers a structured way to manage your agents, so they don't turn into an unreliable mess as you grow their complexity.
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We tested Parlant extensively. The failure patterns that exist in our production logs — capturing them in Parlant takes just a few minutes.
Parlant isn't just a framework. It's a high-level software that solves the conversational modeling problem head-on. Thank you for building it.
Parlant dramatically reduces the need for prompt engineering and complex flow control. Building agents becomes closer to domain modeling.
We went live with a fully functional agent in one week. I'm particularly impressed by how consistent Parlant is with its responses.
Parlant allowed us to deploy a production-ready agent in record time. It turns complex AI scaling into a simple, streamlined process.
Parlant changes how you reason about agent behavior. You get an auditable, iterative process where changes are visible and manageable.
Why teams switch to Parlant
Conversational AI is hard to get right because real-world interactions are diverse, nuanced, and non-linear.
System Prompts
- More rules in the prompt quickly leads to worse adherence to all of them
- You can't pinpoint why the agent said what it said and what needs to change
- The severity of mistakes is unbounded: the agent can say anything when it's ungrounded
- Every fix risks breaking something else
- Only immediately matching context is loaded per turn, so the agent stays focused and consistent even at scale
- Canned response templates deterministically cap failure severity for high-stakes moments
- Every matched context item is traced: what glossary terms were used, which rules fired
Intent Graphs
- Routing to node X means losing context for topic Y, leading to ungrounded responses
- Users mix topics, circle back, and interweave concerns freely
- Great for linear processing workflows, wrong abstraction for real-world dialogue
- Still no control over what the agent says
System Prompt
- More rules in the prompt quickly leads to worse adherence to all of them
- You can't pinpoint why the agent said what it said and what needs to change
- The severity of mistakes is unbounded: the agent can say anything when it's ungrounded
- Every fix risks breaking something else
- Only immediately matching context is loaded per turn, so the agent stays focused and consistent even at scale
- Canned response templates deterministically cap failure severity for high-stakes moments
- Every matched context item is traced: what glossary terms were used, which rules fired
Intent Graphs
- Routing to node X means losing context for topic Y, leading to ungrounded responses
- Users mix topics, circle back, and interweave concerns freely
- Great for linear processing workflows, wrong abstraction for real-world dialogue
- Still no control over what the agent says
Designed to control
real-world interactions.
Parlant offers behavioral control structures you can mix, maintain and reason about.
Define behavioral rules that trigger based on conversational context. When multiple ones apply, Parlant merges them intelligently in-context—no rigid or manual routing required.
# Detect when the agent is discussing high-risk products
discussing_high_risk_products = await agent.create_observation(
"discussing options, crypto, or leveraged ETFs"
)
risk_disclosure = await agent.create_guideline(
condition="customer expresses clear interest in buying high-risk products",
action="provide risk disclosure and verify customer understands potential losses",
tools=[get_high_risk_product_disclosure],
criticality=p.Criticality.HIGH # Pay extra attention to this guideline
)
# Only consider the risk disclosure when the high-risk observation holds
await risk_disclosure.depend_on(discussing_high_risk_products)
Govern every interaction.
Parlant's Conversational AI server sits between your frontend and your LLM provider, managing the entire lifecycle of every interaction, keeping your agent's behavior focused, on-track, and auditable.
Frequently asked questions
Context engineering is the discipline of getting the right context—no more, no less—into the prompt at the right time. You can't cram all of your instructions, knowledge, and tools into one prompt regardless of the model or context window size. Models don't listen beyond a certain point. You need the right items in the prompt (otherwise it's ungrounded and you get hallucinations), and the wrong items out (otherwise you're talking to the wall). Parlant is a context engineering framework: it dynamically assembles only the relevant rules, data, and tools for each turn of the conversation.
Prompt engineering is about getting a model to behave correctly when it already has the right context—optimizing the template, tuning temperature, tweaking the reasoning strategy. Context engineering is about what goes into that template in the first place. A well-engineered prompt with the wrong context will still produce wrong results. They're complementary: prompt engineering handles the how, context engineering handles the what and when.
LangChain and LangGraph are workflow tools—they manage how data flows between steps. Parlant works on a different level of abstraction: It manages behavior. Instead of building a graph of nodes and edges, you define individual behavioral rules (guidelines) and the relationships between them. The framework figures out which rules apply at each turn. This means changing your agent's behavior is a content change (add a guideline), not a structural change (restructure the graph). That's why the 200th rule is as safe to add as the first.
The LLM still handles general conversation naturally. Observations and guidelines define behavioral expectations for specific situations—everything else works as you'd expect from an LLM. If you don't need special handling for a scenario, you don't need to define any rules for it.
Use canned responses with strict composition mode for control of wording. In addition to this, the key often lies in Parlant's response selection mechanism: since each response can reference fields (coming from tool results, retrievers, or guidelines), if a required field isn't present in the current context, Parlant automatically disqualifies that response from being selected. This means that, with proper agent design, the agent can't claim something happened when it hasn't, or vice versa. The guardrails are thus structural and 100% deterministic, not just prompt-based, leading to complete reliability even at a large scale.
The real rigidity of traditional chatbots comes from tree-based flows forcing conversations through predefined branches, not from controlled wording. Even when using the (optional) strict canned responses mode—which lets you control exact wording when needed—the agent still chooses when to use them based on the fluid nature of the interaction, just like call center reps do. The flow stays flexible, but you get precise wording where it matters.
Parlant is LLM-agnostic, so you can use any provider and model—and many teams do. That said, when consistency and reliability matter, some models perform better than others. The officially recommended providers are Emcie, OpenAI (either directly via OpenAI or via a cloud provider, like Azure), and Anthropic (also via AWS Bedrock or others).
