Parlant

Yam Marcovitz and the team at Emcie developed Parlant after watching enterprise customer agents fail as system prompts expanded into unmanageable monoliths. Rather than chaining brittle decision trees, they built an interaction control harness in Python that evaluates context dynamically on each conversational turn. That origin gives backend engineers a deterministic way to deploy LLM-backed agents without surrendering operational control or sensitive data to third-party SaaS vendors.

Why Parlant instead of Decagon

Decagon packages autonomous customer support behind opaque enterprise contracts, charging tens of thousands of dollars annually alongside per-resolution fees that penalize message volume. Teams adopting Decagon must route sensitive conversations through closed infrastructure, accepting vendor lock-in and opaque evaluation. Parlant matches Decagon's core capabilities—executing multi-turn customer journeys and enforcing strict business policies—while running entirely inside your infrastructure. You pay only for underlying model tokens and standard compute, eliminating per-seat fees and volume markups.

The tradeoff centers on operational effort. Decagon provides a managed SaaS environment with turnkey CRM integrations, appealing to support teams that want graphical setup. Parlant gives developers and DevOps engineers code-first control through Python, MongoDB persistence, and OpenTelemetry tracing. Building custom connectors and deploying Docker containers takes engineering time, but the payoff is predictable infrastructure expenditure and complete auditability over every conversational turn.

What it does well

• Dynamic context assembly: Evaluates guidelines as condition-action pairs per turn, injecting only matching instructions into the prompt.
• Rule relationships: Supports explicit dependency and exclusion hierarchies, preventing conflicting instructions from entering the prompt simultaneously.
• Journey orchestration: Defines multi-turn standard operating procedures that adapt to conversational deviations, letting agents advance, backtrack, or resume workflows.
• Canned response enforcement: Enforces strict composition mode on sensitive intents, substituting pre-approved corporate copy to eliminate model hallucination.
• Contextual tool gating: Attaches Python functions to specific observations so models only call external integrations when required conditions trigger.