Migrating from Octonomy to a hybrid AI-human platform: Switching playbook
Migrating from Octonomy to a hybrid AI-human platform requires extracting conversation logic, remapping integrations, and ensuring compliance.
TL;DR: An agentic AI deployment handles volume. What it cannot reliably provide is the node-level decision trail your compliance team needs to answer an EU AI Act auditor's questions about a specific customer interaction. GetVocal's Enterprise AI Agent Platform closes that gap, delivering reliable deflection at scale and documented compliance support across regulated and fast-moving verticals alike. The migration requires moving conversation logic from multi-agent reasoning into explicit, auditable Context Graph decision boundaries before go-live. The result: CCaaS, CRM, and AI unified under a single operational command layer where humans actively direct AI, not just observe it.
Agentic AI adoption across European contact centers has moved past the evaluation stage. The deployment decision is settled. What's surfacing now, across regulated industries from banking to telecom, is a narrower and harder problem: when an EU AI Act auditor requests the full, node-level decision trail for a specific customer interaction, most platforms cannot produce it without custom reporting work. Certification-level GDPR and EU AI Act compliance does not close that gap. Architectural transparency does, and the distance between the two grows each time an enforcement deadline moves closer.
This playbook gives you a concrete, step-by-step migration path from Octonomy to GetVocal's Enterprise AI Agent Platform, covering compliance assessment, integration remapping, Context Graph creation, agent readiness, and go-live validation.
#Why enterprises migrate from Octonomy to GetVocal's Enterprise AI Agent Platform
The architectural challenge with response-generation models is that likelihood-based outputs cannot guarantee policy adherence. When business rules must be enforced consistently, probabilistic response generation introduces compliance risk that regulated enterprises cannot absorb. For regulated European enterprises, that distinction matters when facing potential compliance penalties.
#Compliance gaps in black-box AI
EU AI Act Article 13 requires high-risk AI systems to be transparent, with clear documentation of capabilities, limitations, decision logic, and data logs. Article 14 addresses human oversight requirements for monitoring and interpreting system decisions during operation. Article 50 addresses disclosure when a customer is interacting with AI.
When you cannot trace why the AI chose a specific response path, you cannot produce the documentation these articles demand. Your compliance records will reflect that gap during an audit. We built GetVocal's ContextGraphOS architecture around a different principle: every conversation step is encoded as an explicit, auditable node in a Context Graph. Business logic is structure, generative AI handles natural language, and neither can override the other.
#Preventing AI errors with human checks
GetVocal's two-way Human-AI collaboration model treats human oversight as an active control layer, not a fallback mechanism. When a GetVocal AI agent reaches a decision boundary it cannot handle (a complex complaint, a policy exception, a sentiment drop), it escalates immediately. The AI doesn't always hand off the entire conversation. It can request a validation or a decision from a human, then continue with the customer once that input arrives. Humans are in control, not a backup. That human's decision can inform improvements to AI performance on subsequent similar interactions. This is the Human-AI Flywheel: quality and automation rates can rise together over time. For parallel architecture considerations during migration, our Sierra AI migration guide and Cognigy migration checklist cover comparable transition steps.
#Legacy CCaaS integration barriers
Many agentic AI platforms require integration work with CCaaS layers (including Genesys, Five9, Avaya), CRM systems (such as Salesforce, Dynamics), and knowledge bases. That integration work can be a lengthy phase of AI deployment, and when the AI platform changes, you may need to rebuild those integrations. GetVocal integrates with your existing CCaaS and CRM to coordinate conversation flow while your existing systems remain the source of truth. Your Genesys handles telephony. Your Salesforce holds customer data. Your knowledge base supplies information. The Context Graph coordinates conversation flow.
#Designing your migration roadmap to GetVocal
Glovo scaled from one AI agent to 80 agents in under 12 weeks, achieving 5x increase in uptime and 35% increase in deflection rate (company-reported). That timeline is achievable because GetVocal's pre-built integrations and structured Context Graph creation eliminate the months of custom development work that stalls most migrations. Core use case deployment runs 4-8 weeks with pre-built integrations. Glovo had its first agent live within one week of implementation start (company-reported).
#EU AI Act and GDPR compliance assessment
Before any code moves, your legal and compliance teams should request key compliance artifacts from your new platform vendor. We recommend obtaining these upfront, not in week nine of implementation:
- SOC 2 Type II audit report to verify security controls
- GDPR data processing agreement (DPA) covering data types, processing purposes, duration, and protection obligations
- EU AI Act compliance mapping document showing which platform features satisfy regulatory requirements
- On-premise or EU-hosted deployment architecture diagram confirming data residency options GetVocal's platform is SOC 2 Type II audited and ISO 27001 compliant, GDPR compliant, and engineered for EU AI Act alignment, with on-premise deployment available for telecom, banking, insurance, healthcare, retail and ecommerce, and hospitality and tourism use cases where cloud-only vendors cannot compete.
#Define AI governance approvals
Use the Control Tower's Operator View to define the boundaries of autonomous AI behavior before a single customer interaction takes place. Operators set which conversation steps the AI can handle independently, which require human validation mid-conversation, and which trigger immediate escalation. You set these rules in the configuration layer, not through production failures, which gives your compliance team the documented evidence they need to review and approve pilots with a clear audit trail.
#Define success metrics and KPIs
Set specific, measurable targets before go-live so you can demonstrate ROI at your next board presentation. Based on GetVocal's company-reported performance across customers, realistic targets are:
| KPI | Target | Timeframe |
|---|---|---|
| Deflection rate | 65-70% | Within 3 months |
| First contact resolution | 75%+ | Ongoing |
| Live escalations vs. baseline | 30%+ fewer | From month 2 |
| Self-service resolutions | 40%+ more | From month 2 |
| Time saved per call | 30%+ | From month 2 |
#How to extract Octonomy conversation logic
#Export agent configurations and routing logic
Export every agent configuration your current deployment uses, including supervisor routing logic, specialised agent prompts, knowledge graph sources, and response handling rules. Organise these by use case cluster rather than individual agent, because multi-agent deployments often contain several specialised agents mapping to a single customer need (billing dispute, refund request, account access). You will remap these clusters into explicit Context Graph nodes in the next step. Preserve the historical conversation logs too: these transcripts contain actual customer language patterns, the edge cases your current AI mishandles, and the escalation triggers that are invisible in your agent configurations but obvious in production data.
#Define AI decisioning and escalation frameworks
This is where the architectural shift happens. Multi-agent routing logic becomes deterministic decision trees in the Context Graph. For each use case cluster, define:
- Entry conditions: What triggers this conversation path?
- Data requirements: What customer data does the AI need at each step, and which system provides it?
- Decision boundaries: At which point does the conversation require human judgment, policy validation, or exception handling?
- Escalation triggers: Consider sentiment indicators, repeated resolution attempts, or detection of specific complaint categories.
For your operations managers, GetVocal's Context Graph makes every decision path visible, editable, and testable before deployment. They can review exactly how the AI will handle a billing dispute. Your compliance team can audit every decision point. Nothing is hidden. For a broader view of how this contrasts with low-code platform approaches, see our Cognigy alternatives guide and Cognigy pros and cons assessment.
#Replicate complex interaction patterns
Most agentic AI platforms can struggle with multi-turn, transactional interactions: eligibility checks that require multiple data lookups, billing disputes involving cross-system reconciliation, or post-sales workflows spanning several departments. These are precisely the interactions that carry the highest compliance risk and the highest customer impact. We built GetVocal to handle the full spectrum from simple FAQ to complex transactional workflows. Glovo deployed agents across five distinct use cases simultaneously: partner registration, post-sales documentation, first-level technical support, device recovery, and field service assistance to couriers live during deliveries. Map your complex patterns explicitly in the Context Graph, with clear data access points and escalation triggers at each step.
#Map Octonomy integrations and APIs
Audit every API connection your current deployment uses. Document endpoint URLs, authentication methods, data fields exchanged, and response latency expectations for each connection. Focus on:
- CRM connections and which customer data fields the AI currently accesses
- Ticketing system integrations (Jira, ServiceNow) where the AI creates or updates records
- Knowledge base connections and how content is currently retrieved and formatted for AI responses
- Telephony routing rules that determine when calls transfer between AI and human queues
This audit becomes the integration remapping specification for the new platform.
#Unifying CCaaS and CRM with your new AI
#CCaaS platform integration: Genesys, Five9, Avaya, and more
We built GetVocal to integrate with your existing CCaaS via API without replacing your telephony infrastructure. The Control Tower's unified view consolidates both AI and human agent activity in a single interface. For contact centers still running legacy IVR systems, work with your implementation team to establish connectivity between the IVR and GetVocal's integration layer to pass caller data and routing intent at conversation start, then define clean fallback routing so any conversation the AI cannot handle reaches a human queue with full context. The Movistar Prosegur Alarmas deployment demonstrates effective integration with existing telephony infrastructure.
#Salesforce and Dynamics data mapping
We keep customer data in your CRM. GetVocal pulls case history, account status, and interaction records via API integration during active conversations. Human agents who receive an escalation see the full customer record alongside the AI conversation history in one unified desktop view, without switching applications. For banking, insurance, or healthcare deployments with strict data residency requirements, on-premise deployment options are available to support data sovereignty needs. For more on how this applies across telecom and banking regulated environments, see our compliance-first guide.
#Telephony and omnichannel orchestration
Voice, chat, email, and WhatsApp must be governed under a single system with unified pricing and consistent conversation logic. Deploying separate AI tools per channel can create governance challenges: a policy rule enforced in chat may not apply in voice, and audit trails can become fragmented. We apply the same Context Graph governance across voice, chat, email, and WhatsApp under a single, unified pricing model.
#Ensuring EU AI Act compliant human oversight
#Define escalation triggers and decision boundaries
Build escalation triggers into conversation flows before deployment, not after production failures. Define triggers based on:
- Sentiment threshold: AI can flag conversations where customer sentiment drops below a configured score
- Repetition signals: Repeated failed resolution attempts on the same issue within a conversation
- Topic categories: Configure sensitive complaints to route to a human
- Policy edge cases: Any request requiring a decision outside the AI's explicitly defined authority level.
When escalation triggers fire, the Supervisor View in the Control Tower surfaces the conversation immediately, with full history, customer data, and the specific trigger reason visible to the agent. Agents who receive escalations see the complete conversation context in a single interface. The customer does not repeat themselves. The human documents their decision, and that decision can inform AI improvements.
#Generate compliance audit evidence
Every AI decision in GetVocal generates an automatic audit record showing: the conversation flow path taken, the data accessed at each node, the logic applied, the timestamp, and the escalation trigger if one fired. Your compliance team can retrieve the full decision trail for any interaction during an EU AI Act audit without custom reporting work. GetVocal's AI agents are fully auditable and EU-aligned, covering Article 13 (transparency documentation), Article 14 (human oversight architecture), and Article 50 (AI disclosure at conversation start).
#Preparing agents for AI-assisted customer operations
#Develop migration roadmap for agents
When you brief your team leads, communicate the agent impact clearly and early: AI handles volume growth, not workforce replacement. Frame the shift as redistribution of work, where AI takes high-volume, repetitive interactions and human agents focus on complex, judgment-intensive cases where they add genuine value. When basic queries move to AI, the interactions that reach human agents become disproportionately complex and emotionally demanding, so build structured empathy training into your migration plan covering how to handle distressed customers after a failed AI interaction and how to use conversation sentiment data to prepare before taking an escalation.
#Upskill agents for AI collaboration
For the supervisors and operators your team leads will manage, training on the Control Tower covers two components. Supervisor training typically covers monitoring active conversations, recognising escalation signals, and intervening effectively. Operator training typically covers reviewing and adjusting Context Graph logic based on production data, enabling business teams to iterate on AI behavior. Human agents can observe AI agent performance and provide feedback to improve relevant graph nodes. This is structured knowledge transfer from your experienced human agents to your AI agents. For guidance on which metrics to track under load during this transition, see our agent stress testing metrics guide.
#Validate AI with a single use case
Start with one well-defined, high-volume use case where policy is clear, escalation paths are established, and success metrics are measurable. Password resets, billing inquiries, and order status checks can be strong candidates. Measure weekly: deflection rate, CSAT scores, escalation reasons, and compliance incidents. Expand to additional use cases only after the first achieves consistent, measurable deflection before moving to broader rollout. For scaling considerations once the core deployment is stable, see our guide on conversational AI for seasonal demand.
#Avoiding deployment pitfalls and disruptions
#Go-live safely: Parallel deployment
Run the new platform alongside your existing deployment for a defined transition window. Route a defined percentage of volume through GetVocal while the remainder continues through your current system. This parallel approach validates Context Graph performance against real production data without exposing your full customer base to an untested system. It also gives your agents time to build confidence with the Control Tower before it becomes their primary interface.
#Rigorously validate deflection and FCR
Do not accept vendor deflection claims at face value. Validate your own numbers using the Control Tower's node-level metrics. Track sentiment scores at each conversation step, drop-off rates at specific decision nodes, intent recognition accuracy, and escalation frequency by trigger type. GetVocal supports testing approaches to compare performance and drive incremental improvements after launch.
#Ensure EU AI Act audit readiness
Before full rollout, run a pre-audit check across key areas: transparency documentation (Article 13 compliance logs are accessible), human oversight records (escalations are documented with decision rationale), and AI disclosure protocols (Article 50 compliance is active across channels). Engage your legal team for a final review before decommissioning your previous platform. Our Cognigy vs. GetVocal head-to-head comparison covers how audit readiness requirements compare across platform types.
Comparison: Octonomy agentic AI vs. GetVocal Enterprise AI Agent Platform
| Dimension | Octonomy agentic AI | GetVocal Enterprise AI Agent Platform |
|---|---|---|
| Core architecture | Multi-agent system with Supervisor Agent routing to specialised agents (support, consultancy, ticket processing), context-sensitive knowledge graphs, structured reasoning | Context Graph combining deterministic conversational governance with generative AI for natural language fluency |
| Decision auditability | Agent-based workflow | Full glass-box audit trail for every conversation node |
| EU AI Act readiness | GDPR and EU AI Act compliant, ISO 27001, ISO 27701, ISO 9001, SOC 2 compliant | Engineered for Articles 13, 14, and 50 with compliance logging |
| Human oversight model | Human escalation with conversation handover | Active two-way collaboration via Control Tower |
| Automation coverage | Heavy industry and manufacturing workflows, customer and field service, technical support | Broad CX coverage including complex transactional workflows |
| Deployment model | Cloud-based SaaS (one-tenant-per-cluster architecture, on-premise not publicly documented) | Cloud, on-premise, or EU-hosted options |
| Integration approach | Enterprise system integrations | Pre-built connectors for major CCaaS and CRM platforms |
Migrating from Octonomy is not a technology swap. It is an architectural decision about what kind of AI your compliance team can stand behind and your customers can trust. Enterprises that complete this migration in 2026 leave with both a documented deflection rate (65-70% within three months, based on company-reported performance) and a compliant audit trail. Starting the compliance and integration assessment now gives your team time to address dependencies before enforcement timelines tighten.
Schedule a 30-minute technical architecture review with our solutions team to assess integration feasibility with your specific CCaaS and CRM platforms or request the Glovo case study to see the implementation timeline, integration approach with Genesys and Salesforce, and KPI progression.
#FAQs
How long does an Octonomy-to-GetVocal migration take?
Core use case deployment runs 4-8 weeks with pre-built integrations for CCaaS platforms including Genesys, Five9, and Avaya, and CRM platforms including Salesforce and Dynamics, among others. Glovo scaled from one to 80 agents in under 12 weeks across five use cases (partner registration, post-sales documentation, first-level technical support, device recovery, and field service assistance to couriers live during deliveries), with the first agent live within one week of implementation start (company-reported).
Can Octonomy conversation logic transfer directly to GetVocal?
Because architectural models differ significantly across platforms, your existing agent configurations, supervisor routing logic, specialised agent prompts, knowledge graph sources, need to be re-mapped into explicit Context Graph nodes with defined decision boundaries and escalation triggers. This requires structured analysis of your existing flows rather than a simple file transfer.
What EU AI Act compliance records does GetVocal generate automatically?
The platform generates continuous audit logs for every AI decision, including conversation path taken, data accessed at each node, logic applied, timestamp, and escalation trigger if one fired. These records satisfy the Article 13 documentation requirements for high-risk AI systems without custom reporting work.
How do you minimise CX disruption during the migration?
Run parallel deployment, routing a defined percentage of volume through GetVocal while the rest continues on Octonomy. Validate Context Graph performance against real production data before full cutover. Use the Control Tower to monitor escalation rates and sentiment in real time. Core use case deployment runs 4-8 weeks with pre-built integrations.
#Key terms glossary
Context Graph: GetVocal's protocol-driven conversation architecture powered by ContextGraphOS, encoding your business rules, policies, and procedures as explicit, auditable decision nodes. Each step shows what data the AI accesses, what logic it applies, and when it escalates to a human.
Control Tower: GetVocal's operational command layer where human judgment is applied to AI-driven conversations, both in configuration (Operator View) and in real time (Supervisor View). It is not a monitoring dashboard. It is the interface through which humans actively direct AI behavior.
Deterministic governance: An architectural approach where business logic is encoded as explicit, enforceable rules rather than probabilistic predictions. In GetVocal's model, LLMs handle natural language fluency while the Context Graph enforces policy adherence, and neither can override the other.
EU AI Act Article 50: The transparency requirement mandating that enterprises disclose to users when they are interacting with an AI system, unless it is obvious from context. Compliance with Article 50 is an important component of EU AI Act readiness.
Human-AI Flywheel: The continuous learning mechanism where every human intervention in the Control Tower updates the relevant Context Graph node, improving AI performance on subsequent similar interactions so that automation rates improve after launch, not just at launch.