Dialogflow ES to Conversational Agents Migration Services

Why Conversational Agents?

Build More Capable Conversational Experiences

Advanced Conversation Control

Use flows, pages, routes, conditions, and event handlers to manage complex, multi-step conversations more reliably.

Generative and Deterministic Design

Combine controlled conversation flows with generative playbooks for flexible interactions without losing business-rule oversight.

Enterprise Data Grounding

Connect agents with approved data stores and enterprise information to provide more relevant and contextual responses.

Better Testing and Governance

Use test cases, environments, versions, validation, change history, security settings, and controlled deployment processes.

How We Migrate

A Structured Dialogflow ES Migration

1

Assess

Review intents, entities, contexts, parameters, events, fulfillment, knowledge sources, integrations, languages, environments, and usage.
2

Design

Translate the existing experience into flows, pages, routes, forms, playbooks, data stores, webhooks, and reusable conversation components.
3

Migrate Agent Assets

Move supported intents and entities, then rebuild contexts, parameters, responses, events, fulfillment, and conversation transitions.
4

Update Integrations

Adapt webhooks, APIs, channels, authentication, telephony, analytics, and connected business systems for the target platform.
5

Test and Cut Over

Validate complete conversations, regression scenarios, integrations, latency, fallback handling, and business outcomes before production release.

Why Codimite?

End-to-End Conversational AI Modernization

Codimite combines Google Cloud expertise, conversation design, application integration, generative AI, and structured testing.

Start Your Migration

Complete Agent Discovery. We identify active intents, contexts, entities, fulfillment logic, channel dependencies, fallback paths, and unused assets.
Conversation Architecture Redesign. We convert intent-driven interactions into clear flows, pages, routes, conditions, forms, playbooks, and escalation paths.
Fulfillment and Integration Migration. We update webhook contracts, session data, authentication, API calls, channel integrations, and downstream business processes.
Controlled Production Rollout. We reduce risk through automated tests, conversation comparison, pilot users, phased traffic, monitoring, and rollback planning.

Platform Comparison

Dialogflow ES and Conversational Agents at a Glance

Both platforms support virtual agents, but Conversational Agents provides stronger conversation orchestration and generative AI capabilities for complex use cases.

Area	Dialogflow ES	Conversational Agents
Platform approach	Intent-based natural language understanding platform for smaller and simpler agents	Conversational AI platform combining structured flows, generative playbooks, and grounded data
Conversation structure	Intents act as the primary building blocks of each conversation turn	Flows and pages organize conversations into explicit, reusable states
Conversation control	Contexts control which intents can be matched during a session	Routes, conditions, events, forms, and page scope provide more precise control
Complex conversations	Suitable for relatively simple intent-led interactions	Designed for larger, multi-step, and branching conversational experiences
Generative AI	Primarily deterministic intent and response design	Supports generative playbooks alongside deterministic flows
Knowledge grounding	Knowledge connectors support selected document-based responses	Data stores and generative capabilities support grounded conversational answers
Intent design	Intents contain training phrases, parameters, contexts, events, responses, and fulfillment settings	Intents remain reusable while routing, fulfillment, and state logic are handled separately
Parameters	Parameters are defined within intents and can be stored in contexts	Intent parameters, page forms, and session parameters support structured data collection
Slot filling	Required intent parameters trigger prompts	Page forms collect required parameters with validation and reprompt handling
Fallback handling	Uses fallback intents	Uses no-match and no-input event handlers within the active flow or page
Follow-up intents	Provides predefined follow-up intent structures	Uses reusable intents and scoped routes for contextual follow-up behavior
Responses	Responses are commonly configured within matched intents	Fulfillment can be triggered from routes, events, pages, and webhook results
Webhooks	ES-specific webhook request and response formats	Updated webhook structures with fulfillment tags and session parameters
Conditional logic	Relies heavily on contexts and fulfillment code	Native conditional routes and state handlers support explicit branching logic
Testing	Provides console simulation and basic agent testing	Supports simulator testing, reusable test cases, validation, and regression testing
Environments	Supports versions and environments for deployment	Supports versions, environments, environment-specific webhooks, and controlled releases
Analytics	Provides agent interaction and intent-level analytics	Supports richer conversation analysis across flows, pages, routes, and agent behavior
Change management	Agent configuration and export-based management	Change history, validation, versions, environments, and structured development controls
Integrations	Supports ES-specific channel and telephony integrations	Supports Conversational Agents integrations, APIs, webhooks, and contact-center use cases
Security	Google Cloud IAM and Dialogflow ES security controls	IAM, security settings, data redaction, retention controls, auditability, and Google Cloud governance
Best suited for	Smaller FAQ bots and straightforward intent-based interactions	Complex customer journeys, service automation, contact centers, and generative conversational applications

FAQs

Dialogflow ES to Conversational Agents Migration FAQs

What can be migrated from Dialogflow ES to Conversational Agents?

Codimite can migrate intents, training phrases, entities, parameters, fulfillment logic, responses, events, integrations, environments, knowledge workflows, and connected applications.

Can a Dialogflow ES agent be imported directly into Conversational Agents?

Not completely. Selected intents, training phrases, parameters, and custom entities may be transferred, but contexts, fulfillment, conversation paths, fallback behavior, and integrations usually require manual redesign.

What happens to Dialogflow ES contexts, intents, and required parameters?

Contexts are redesigned using flows, pages, routes, conditions, forms, and session parameters. Intent training phrases may be reused, while required parameters are rebuilt as structured form fields with prompts and validation.

What happens to Dialogflow ES fulfillment webhooks and integrations?

Existing business logic may remain reusable, but webhook formats, authentication, parameters, responses, error handling, telephony, channels, and application APIs often require updates.

Can generative AI be added during a Dialogflow ES migration?

Yes. Generative playbooks, grounded data stores, and flexible responses can be added to suitable interactions, while transactional and business-critical journeys remain controlled through deterministic flows.

How do you validate a Dialogflow ES to Conversational Agents migration?

We test intent matching, parameter collection, responses, webhook calls, fallback handling, integrations, latency, complete conversation paths, and business outcomes before production cutover.