Building AI agents step-by-step: From conversation mapping to production deployment
Building AI agents requires a structured 7-phase process from conversation mapping to deployment with EU AI Act compliance built in.
TL;DR: Building production-ready AI agents requires a structured 7-phase process that combines deterministic conversation governance with generative AI capabilities, giving operations teams the speed of modern LLMs with the auditability that compliance teams and regulators require. The organizations that succeed follow this sequence: define use cases with compliance guardrails, map conversation flows into transparent Context Graphs, integrate for audit-ready data flow, validate with production data, deploy in controlled phases, and govern through active human oversight.
Enterprise AI deployments now operate under two distinct pressures: regulated industries facing a hard EU AI Act enforcement deadline of August 2, 2026, alongside legacy technical debt that complicates integration and audit readiness, and faster-moving verticals like retail, ecommerce, and hospitality facing seasonal volume spikes and competitive pressure to stand up AI-assisted customer operations before the next peak period. This guide breaks down the exact step-by-step process to build, integrate, and deploy AI agents using a hybrid human-AI architecture that delivers measurable results across both tracks, whether your primary driver is compliance or speed-to-value.
Building enterprise AI agents that scale requires combining generative AI capabilities with deterministic conversation governance. That combination of generative capability and transparent governance determines which deployments survive audit review and which get shut down before they scale.
#AI agents: map for transparency and auditability
#AI agent vs. chatbot: key differences
The terms "chatbot" and "AI agent" are used interchangeably in vendor marketing, but the architectural distinction determines whether your compliance team approves your deployment or shuts it down.
Generative AI is reactive. It waits for a prompt and produces content. Ask it to handle a billing dispute, and it generates a plausible-sounding response based on patterns in its training data. That response may contradict your actual refund policy, and you won't know until a customer complains or your legal team catches it in production.
Agentic AI is proactive, acting without waiting for a prompt. It monitors context, executes multi-step workflows, and takes action: querying your CRM, scheduling a callback, triggering a refund, escalating to a human supervisor. Any architecture supporting these multi-step workflows requires full auditability at every action point. That requirement determines whether your compliance team approves or blocks deployment. The distinction matters because agentic AI systems are subject to the EU AI Act high-risk classification when they influence decisions affecting customers in regulated sectors like banking, telecom, and insurance.
When a system connects to other platforms and takes action based on user input, it generally falls under stricter compliance obligations, particularly when deployed in high-risk sectors such as banking, telecom, and insurance.
#Ensure AI auditability via mapping
EU AI Act Article 13 requires high-risk AI systems to be designed so their operation is sufficiently transparent, allowing deployers to understand and appropriately use their outputs. Black-box LLMs fail this requirement by design: you cannot trace why a model produced a specific output, which means your compliance team cannot generate the audit documentation regulators will demand.
Conversation mapping solves this at the architecture level. When every decision path in your AI agent is visible, editable, and traceable before deployment, you satisfy Article 13 without retrofitting compliance onto a system that was never designed to support it. We built our approach to conversational AI for regulated industries around this principle from day one.
#Agent architectures: Graph vs. RAG
RAG (Retrieval-Augmented Generation) works by embedding a query, retrieving similar documents from a vector database, and feeding those documents into an LLM as context. It improves factual accuracy over bare LLMs but introduces its own failure mode: putting incorrect, irrelevant, or too much information into the context window can actually degrade rather than improve results.
Context engineering goes further, using knowledge graphs to understand how different pieces of information are connected, linking entities and relationships across documents and databases to make retrieval more accurate and explainable for both humans and machines.
This architectural principle extends further when business rules are encoded as deterministic nodes in a conversation graph. Each node specifies what data to retrieve, what logic to apply, and when to escalate to a human. GetVocal's Context Graph applies this model directly, producing a living graph of conversation protocols you can stress-test against historical transcripts before a single live customer interaction occurs.
#Step 1: Define your use case and success metrics
#Pinpoint quick-win AI agent use cases
Start with high-volume, low-complexity interactions where policy is unambiguous and escalation paths are already well-defined. Password resets, billing inquiries, and appointment scheduling work well for regulated industries. Order tracking, delivery status updates, and booking management are the equivalent starting points for retail, ecommerce, and hospitality operations. All of these generate enough volume to produce meaningful performance data within 30 days, and the consequences of an AI error are recoverable, not catastrophic.
Avoid starting with complaint handling, fraud disputes, or any interaction that triggers a human judgment call about policy exceptions. Build your team's confidence in the platform on interactions where the policy is clear before expanding to cases where it isn't.
#Set baseline KPIs: deflection, AHT, FCR
Before configuring a single node, document your current performance in writing so your CFO has a before-and-after comparison at the 90-day review.
The KPIs that matter at this stage are:
- Deflection rate: What percentage of contacts does the AI resolve without human involvement? Glovo, a delivery and logistics platform managing high-volume, time-sensitive customer contacts, achieved a 35% increase in deflection rate within weeks of deployment (company-reported). For retail, ecommerce, and hospitality operations facing seasonal spikes, this demonstrates what fast deployment on clear, repetitive use cases can deliver.
- Average handle time (AHT): Movistar Prosegur Alarmas reduced median handle time by 30% after replacing their legacy IVR with a GetVocal virtual assistant (company-reported).
- First-contact resolution (FCR): GetVocal reports 77%+ first-contact resolution (FCR) across the platform (company-reported), with 65% average query resolution rate overall.
- Escalation rate: Track this separately by intent type so you can identify which use cases are generating unnecessary human handoffs.
#Define EU AI Act and GDPR requirements
EU AI Act Article 14 requires high-risk AI systems to be designed to allow human oversight during operation, with humans able to monitor, interpret, and override the system. Document your compliance gaps at this stage, not after go-live.
Three specific questions to answer in writing before building:
- Does your data processing agreement (DPA) cover AI-driven customer interactions across all countries where you operate?
- Can your architecture generate a complete audit log for every AI decision, including data accessed, logic applied, and escalation trigger?
- Do you have an on-premise or EU-hosted deployment option to satisfy data residency requirements in banking and healthcare?
#Template for AI agent use cases
Document each use case before building it:
- Use case name: e.g., "Billing inquiry, balance and payment status."
- Current volume: Interactions per month
- Current channel: Voice, chat, WhatsApp, or email
- Policy document source: Link to the internal knowledge base article or CRM process
- Decision boundaries: List the specific conditions that require human escalation
- Success metric: Target deflection rate, AHT reduction, or FCR improvement at 90 days
- Compliance classification: High-risk AI system (yes/no) with Article 13/14 applicability
#Step 2: Map conversation flows and decision points
#Audit existing IVR and chat transcripts
Pull a representative sample of historical transcripts from your legacy CCaaS platform or channel archives for the chosen use case. Group them by intent and outcome, then identify the five most common conversation paths. These paths become the backbone of your Context Graph.
Flag every instance where a human agent deviated from the standard script. Those deviations reveal the edge cases your AI must handle correctly, or escalate rather than guess. This audit process is exactly how you extract reusable logic from technical debt rather than discarding years of institutional knowledge in a rip-and-replace migration. For more on this, our guide on migrating from legacy platforms covers the risk mitigation steps in detail.
#Identify escalation triggers and edge cases
Define escalation triggers before you map any conversation node. These are the conditions under which the AI must stop and route to a human without attempting to resolve the interaction itself.
Common trigger categories for regulated CX:
- Customer expresses frustration above a defined sentiment threshold
- Intent matches a complaint or dispute category
- Policy exception is required (refund outside standard window, waiver request)
- Customer asks directly to speak with a human
- Data mismatch between what the customer states and what the CRM shows
#Visualize AI agent decision paths
The GetVocal Agent Builder translates your documented conversation paths into a visual Context Graph where every node shows the data it accesses, the logic it applies, and the conditions that trigger escalation. GetVocal designed the interface so operations managers and compliance leads can review these graphs without engineering support, which is the practical difference between a governance tool and a black-box system. For a direct comparison of how this approach contrasts with Cognigy's low-code development platform, see our Cognigy vs. GetVocal analysis.
#Embed human control in AI flows
Our Control Center's Operator View is where you configure and manage the rules governing autonomous AI behavior. This is not a monitoring screen. It is the configuration layer where you define and refine what the AI is and is not permitted to do, adjusting decision logic as your use cases evolve and operational needs change. GetVocal designed escalation paths to be built into conversation flows here, not bolted on as a fallback after incidents occur.
#Auditable conversation map framework
- Identify entry intent (what triggered the interaction)
- Map data retrieval step (what information the agent needs from CRM or knowledge base)
- Define decision node (what logic determines the response or next step)
- Set escalation condition (what triggers human handoff)
- Document action taken (resolution, transfer, scheduled callback)
- Log outcome (resolved, escalated, abandoned) for performance analysis
#Step 3: Define the agent's decision logic
#Convert conversation maps to nodes
Each node in the Context Graph represents one discrete step in the conversation. For a billing inquiry agent, representative nodes would cover authentication, account lookup, balance retrieval, payment status, payment options, confirmation, and resolution logging. At each node, you configure three elements: the data source the agent queries, the deterministic logic it applies, and the generative language model layer that produces the natural language response. The deterministic layer enforces policy. The generative layer makes the interaction sound human.
#Balance deterministic vs. generative responses
This is the architectural decision that separates GetVocal from pure LLM solutions and from Cognigy's low-code development platform approach. GetVocal gives you control over the mix at the node level, from 100% deterministic (for policy-critical steps like eligibility determination) to 100% generative (for conversational acknowledgments where variation is harmless).
For regulated CX, policy-critical nodes must be deterministic. If your refund policy states a 14-day window, that logic must be encoded as a rule, not fed as context to an LLM that might reason around it. Our guide on Cognigy alternatives explains why this matters specifically for European enterprise compliance requirements.
#Agent failure modes and contingencies
OpenAI's framework distinguishes between routine tasks (repetitive, deterministic workflows suitable for automation) and complex tasks (multi-step workflows requiring reasoning and decision-making). Your Context Graph must account for both.
Map explicit failure modes for each use case:
- Data retrieval failure: CRM returns null or timeout. Define fallback behavior: collect information manually, then escalate with context.
- Intent ambiguity: Customer input matches multiple intents. Define a disambiguation node or escalate with partial context.
- Policy edge case: Request falls outside defined parameters. Route to a human with a full conversation history attached.
#AI agent blueprint walkthrough
Your team reviews and adjusts Context Graph logic for your specific use case rather than building from a blank canvas, which compresses the design phase. See how this approach compares against alternatives in our PolyAI vs. GetVocal comparison.
#Step 4: Integrate systems for compliant data flow
#Audit-ready CRM-telephony integration
Your Context Graph is only as good as the data it can access in real time. GetVocal integrates with your existing CCaaS platform (including Genesys, Five9, NICE CXone) and CRM (including Salesforce, Dynamics 365) via bidirectional sync, ensuring that the AI and human agents see the same customer record simultaneously. Document your integration requirements in this order:
- Telephony platform and API version
- CRM and field mapping for customer context
- Knowledge base location (Confluence, SharePoint, proprietary system)
- Authentication system (SSO, OAuth, customer-facing verification)
For context on how this integration model compares to Cognigy's approach, our Cognigy migration checklist details the integration steps and risk mitigation required when transitioning between platforms.
#Define agent API integration points
An AI agent that can only respond to text is a chatbot. An enterprise AI agent takes action: querying databases, scheduling callbacks, triggering refunds, and updating CRM records. Define each action type your agent must perform, the API endpoint it calls, the authentication method, and the error handling protocol if the call fails.
#Configure AI knowledge sources
Connect your knowledge base to the Context Graph through controlled retrieval, not open-ended search. Index only the policy documents relevant to each use case. This prevents the agent from retrieving outdated policy versions or articles from adjacent product lines that could produce contradictory responses.
For IVR modernization use cases, knowledge base configuration is often a lengthy step because legacy contact centers accumulate years of overlapping, inconsistent policy documentation that must be audited and reconciled before the AI can use it reliably.
#Ensure AI data access compliance
On-premise deployment means GetVocal runs behind your firewall, with customer data never leaving your infrastructure. This directly addresses data residency requirements for banking, insurance, and healthcare use cases where cloud-only vendors cannot compete. GetVocal built GDPR, SOC 2, and HIPAA compliance into the architecture from the start, not patched in after procurement requirements surfaced. GetVocal also offers EU-hosted deployment for organizations where a full on-premises deployment is not operationally feasible.
#AI agent integration blueprint
Pre-deployment integration checklist:
- Telephony API authentication tested and documented
- CRM bidirectional sync verified with test customer records
- Knowledge base indexed and version-controlled
- DPA covers AI-driven interactions in all deployment geographies
- Audit log generation confirmed for every node type
- On-premise or EU-hosted deployment configured and verified
- GDPR data subject request workflow documented
#Step 5: Validate agent performance with real-world data
#Run regression tests on historical transcripts
Feed your historical transcript sample from the Step 2 audit through the Context Graph before exposing it to live traffic. The agent should handle the most common paths correctly and escalate the edge cases cleanly. Any path that produces an incorrect resolution or fails to escalate a defined trigger is a node-level defect you fix before go-live, not after.
For guidance on which performance metrics matter most under load conditions, our article on agent stress testing metrics provides a practical framework for identifying failure points before they reach customers.
#Test edge cases and policy contradictions
Your historical transcripts will surface cases where two policy rules conflict. Document every conflict you find and resolve it at the policy layer before encoding it in the Context Graph. Encoding an unresolved contradiction creates a deterministic failure mode that is harder to debug than an LLM hallucination because it fails consistently rather than probabilistically.
#Legal and GDPR compliance validation
Before moving to production, generate a sample audit log and walk your compliance team through it. The log must show: intent classification, data sources accessed at each node, logic applied, escalation trigger (if applicable), resolution outcome, and timestamp. If your compliance team cannot answer "why did the AI say that?" from the log alone, the log is insufficient.
#Mitigate AI risk with user tests
Run an internal pilot with agents handling live interactions under supervised conditions before opening to full traffic. The Control Center's Supervisor View gives supervisors real-time visibility into every active conversation, with the ability to intervene, redirect, or take over without disrupting the customer experience. Use this phase to calibrate escalation thresholds and identify intent patterns not present in historical transcripts.
#Comprehensive AI agent pre-flight checklist
- Regression tests passed on the majority of your historical transcript sample
- All defined edge cases escalate correctly
- Audit log reviewed and approved by compliance lead
- Legal signed off on DPA coverage for AI interactions
- Sentiment thresholds configured and tested in the Control Center
- Human escalation routing confirmed in telephony integration
- Rollback procedure documented and tested
#Step 6: Deploy in controlled phases
#Controlled go-live: 5-10% initial traffic
Route 5-10% of live traffic to the AI agent for the first week. Your telephony integration maintains fallback routing to human agents for all remaining interactions. This is not a soft launch, it is a controlled experiment with a defined success threshold that determines whether you expand or pause.
Our standard deployment timeline runs 4-8 weeks for a core use case with pre-built integrations. Glovo deployed their first agent within one week and scaled to 80 agents in under 12 weeks, achieving a 5x increase in uptime and a 35% increase in deflection rate (company-reported). That pace is faster than typical and reflects Glovo's existing data infrastructure and defined use cases, not a universal baseline. Retail, ecommerce, and hospitality deployments without legacy IVR debt frequently land within that 4-8 week core range, particularly for well-defined use cases like order tracking, delivery status, or booking management. Multi-use-case deployments with complex legacy IVR migrations typically run 16-20 weeks.
#Analyze first 30-day agent metrics
At 30 days, pull these metrics by intent type, not in aggregate:
- Deflection rate per use case
- Escalation rate and escalation reason breakdown
- CSAT scores for AI-handled vs. human-handled interactions
- Average handle time for AI-resolved interactions
- Node-level drop rate (where customers abandon the conversation)
Node-level drop rates are the most actionable early signal. A high drop rate at a specific node means either the agent's response is confusing customers or the node's escalation threshold is too aggressive. Both are fixable at the Context Graph level without retraining a model.
#AI agent hypercare runbook setup
Hypercare is a defined post-launch support phase where dedicated resources monitor system stability and resolve issues with urgency. A three-month hypercare period is standard for enterprise AI deployments, with an extension clause if escalation volumes have not stabilized to normal BAU levels.
Your hypercare runbook must define:
- Issue severity classification (P1 through P4) with response SLAs
- Escalation path from support to engineering
- Daily monitoring cadence for the first 30 days
- BAU support transition criteria covering when ticket volume has stabilized sufficiently to exit hypercare
#Expand AI agent coverage
Expand to 25%, 50%, and 100% traffic only after the 30-day metrics confirm the success thresholds you defined in Step 1 are met. Once the first use case is stable, apply the same 7-step process to add the second use case. Context Graph for a billing inquiry agent and a technical support agent will share authentication and CRM integration nodes, which compresses design and integration time for each subsequent deployment. See how this compares to the PolyAI approach in our PolyAI alternatives guide for context on deployment architecture differences.
#AI agent deployment readiness checklist
- 30-day metrics meet defined success thresholds
- No open compliance findings
- Hypercare team in place with defined SLAs
- Rollback procedure confirmed with telephony team
- Expansion timeline approved by operations lead
#Step 7: Monitor and evolve live agent performance
#Monitor agent conversations for issues weekly
The Control Center's Supervisor View surfaces active conversations, flags escalations, and gives supervisors the tools to step in or redirect without disrupting the customer experience. This is an operational command layer, not a monitoring dashboard. Human judgment is applied to live AI-driven conversations continuously, not retrieved after incidents occur.
Set a weekly review cadence for the first three months, covering escalation logs by reason, sentiment trend by use case, and node-level drop rates. Any metric trending negatively for two consecutive weeks triggers a Context Graph review, not a wait-and-see response.
#Pinpoint agent handoff causes
Categorize every escalation by cause and track the top causes weekly. Most enterprise deployments find that the majority of escalations concentrate in a small number of root causes: sentiment thresholds triggering too early, policy gaps in the knowledge base, or missing intent mappings in the Context Graph. Each is fixable within days once identified. Compare the agent escalation experience across platforms with our platform comparison analysis for context on how escalation architecture differs across vendors.
#Data-driven AI agent logic updates
Every human agent intervention generates production data. When a supervisor takes over a conversation, their decision is logged against the Context Graph node that triggered the escalation. Our continuous learning infrastructure runs A/B tests automatically: two versions of a node handling the same intent pattern, with the better-performing version becoming the new default.
This is fundamentally different from prompt rewriting or whole-model updates. You're making surgical, node-level improvements to your Context Graph based on real customer interaction data, with full visibility into what changed and why.
#Track ROI at 30/60/90 days
This framework applies across verticals. Whether you're managing compliance-driven deflection targets in telecom or insurance, or handling seasonal volume spikes in retail and ecommerce, the KPI progression follows the same pattern. Present it to your CFO at each review:
| Metric | 30-day | 60-day | 90-day |
|---|---|---|---|
| Deflection rate | Baseline established | Increasing | Target 65-70% (company-reported platform average) |
| AHT vs. baseline | Tracked | Reducing | 30%+ reduction achievable (Movistar result, company-reported) |
| Escalations vs. baseline | Tracked | Reducing | Target 31% below baseline (company-reported platform average) |
| Cost per interaction | Baseline documented | Reducing | Positive ROI typically visible within 1-2 months of stable deflection |
| Compliance incidents | Zero | Zero | Zero |
GetVocal reports an average 45% more self-service resolutions and 31% fewer escalations versus traditional solutions across its customer base (company-reported). These results hold across regulated industries and faster-moving verticals alike. Contact our team for a custom cost model based on your resolution volume.
#Prevent AI build failures and compliance risks
#Mapping prevents AI agent failures
AI pilot failures in regulated environments follow a consistent pattern: black-box LLMs contradict policy not because they're poorly configured, but because they're architecturally incapable of guaranteeing policy adherence. When legal teams shut down AI deployments, the underlying cause is usually the absence of a deterministic conversation map. Our Context Graph eliminates this failure mode by making policy enforcement a structural property of the system, not a probabilistic outcome. For a detailed comparison of how this plays out against Cognigy's low-code development platform approach, see our Cognigy pros and cons assessment.
#Auditing complex AI integrations
API uptime targets above 99.5% require both your integration layer and your telephony provider to meet SLA commitments. Document SLA credits and escalation procedures in your vendor contracts before go-live. Integration incidents follow defined escalation procedures documented during implementation.
#Clean data testing: avoid production failures
Never test only on clean, hand-selected transcripts. Your production environment contains malformed inputs, mid-sentence topic switches, customers who provide inaccurate information, and interactions that span multiple sessions. Pull test data from production systems and include your lowest-quality historical samples. Testing on representative production data reduces the risk of failures that only surface in live environments. Without realistic test inputs, outcomes in your controlled environment will not represent how customers actually interact with the system. The goal is to stress-test the Context Graph against edge cases before your customers encounter them, not to guarantee zero production surprises from a sanitized test set.
#AI agent project shutdown risk
Gartner's cancellation data shows over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. Projects that skip compliance architecture, bypass phased rollout, and lack defined escalation paths are the ones that fail, often before they reach meaningful scale. The 7-phase process in this guide is specifically designed to address each of those failure points.
#Addressing AI agent compliance concerns
#Realistic AI agent deployment timeline
Core use case deployment runs 4-8 weeks with pre-built integrations. This covers discovery, Context Graph creation, integration configuration, internal testing, and phased go-live. Glovo's first agent was live within one week, which reflects rapid early deployment within that broader timeline. Multi-use-case deployments with complex legacy IVR migrations and multi-country data residency requirements typically run 16-20 weeks, with the first use case live at week 8 and subsequent use cases deploying in parallel. For a detailed look at how the migration timeline plays out in practice, our Sierra AI migration guide covers the dependency mapping and risk mitigation steps.
#Agent integration requirements
Technical prerequisites for enterprise CX deployment:
- CCaaS platform with documented API access (including Genesys, Five9, NICE CXone, and more)
- CRM instance with clean customer records and defined field mapping
- Knowledge base with version-controlled policy documentation
- GDPR data processing agreement covering AI-driven interactions
- On-premise or EU-hosted infrastructure decision confirmed
For regulated industries specifically, our telecom and banking conversational AI guide details the additional compliance prerequisites that distinguish enterprise deployments from standard SaaS implementations.
#Agent transparency for EU AI Act
Article 13 requires that providers supply clear instructions covering performance characteristics, accuracy, robustness, and cybersecurity expectations. Article 14 requires that humans assigned oversight responsibilities can monitor anomalies, avoid automation bias, correctly interpret outputs, and decide not to use the AI system in any given situation. Our glass-box architecture addresses both requirements at the design level. Every decision path in the Context Graph is visible, editable, and traceable in real time, providing the documentation your legal team needs before regulators ask for it.
#Designing agent handover paths
GetVocal designed human-in-the-loop as an operational layer, not a backup system. When the AI agent reaches a decision boundary, it does not always hand off the entire conversation. The response depends on what the boundary requires. For decisions that need a single validation or approval, the AI pauses, surfaces the relevant context to a human agent, receives the input, and then continues the conversation directly with the customer. For more complex situations, including emotional escalations, policy exceptions, or unresolved edge cases, the AI routes the full conversation to a human with the complete interaction history, customer CRM data, and the specific escalation trigger attached. The human does not repeat questions. In both patterns, the AI continues shadowing the interaction and logs the human's decision against the relevant Context Graph node. That decision improves the agent's future performance at that exact node. For a comparison of how this two-way handover model differs from single-direction escalation architectures, see our Sierra AI alternative analysis.
These two patterns describe the most visible handover moments, but the full collaboration model includes three additional behaviors that don't involve a full conversation transfer. First, the AI can request a mid-conversation validation or approval from a human agent, pause at that decision point, and resume the interaction once it receives the input, without the human ever taking over the conversation. The customer experiences no gap. Second, once a human agent resolves the element requiring judgment, a supervisor or operator can reassign the conversation back to the AI with full context intact, so the AI continues the remainder rather than the human handling it to completion. This means escalation is not a one-way door. Third, supervisors can shadow any live conversation in real time through the Control Center's Supervisor View, observing the AI's decision logic as it executes, intervening if needed, or confirming the AI should continue, all without friction to the customer. Across all six behaviors, the design philosophy is the same: Human in control, not backup.
#Ready to deploy your first AI agent?
Request the Glovo case study to see how a high-volume delivery and logistics operation deployed their first agent within one week and scaled to 80 AI agents in under 12 weeks, including the complete implementation timeline, Context Graph architecture diagrams, and documented KPI progression at 30/60/90 days. The deployment speed and phased rollout approach apply directly to retail, ecommerce, and hospitality operations managing seasonal volume spikes, as well as regulated enterprises prioritising compliance documentation. Or schedule a technical architecture review with our solutions team to assess integration feasibility with your specific CCaaS and CRM platforms.
#FAQs
How long does it take to build and deploy a single enterprise AI agent?
Core use case deployment runs 4-8 weeks with pre-built integrations, covering discovery through phased go-live. Multi-use-case deployments with legacy IVR migration typically run 16-20 weeks, with the first use case live at week 8.
What is the difference between a Context Graph and a RAG-based AI agent?
A Context Graph encodes your business rules as deterministic nodes with auditable decision logic at each step. RAG retrieves documents from a vector database and feeds them to an LLM, which improves accuracy but cannot guarantee policy adherence or generate the audit trails EU AI Act Article 13 requires.
What is the minimum contract commitment for GetVocal?
GetVocal requires a 12-month minimum commitment. Contact the team for details.
Can GetVocal run on-premise behind our firewall?
Yes. We offer on-premise deployment for banking, healthcare, and government use cases where cloud-hosted solutions cannot satisfy data residency or data sovereignty requirements.
What deflection rate should I expect in the first 90 days?
GetVocal reports an average 65% query resolution rate across its platform (company-reported), with 70% deflection achievable within three months of launch for well-defined use cases. Glovo achieved a 35% increase over their existing baseline within weeks (company-reported).
What happens to agentic AI projects that skip compliance architecture?
Gartner's analysis shows over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. The primary failure mode is deploying to full traffic without a phased rollout or a compliance architecture, then getting shut down when a policy contradiction reaches a customer.
#Key terms glossary
Agentic AI: AI systems designed to autonomously make decisions and take action across multi-step workflows, distinct from generative AI which only produces content in response to prompts.
Context Graph: Our graph-based protocol architecture that encodes business rules as deterministic, auditable nodes governing every step in an AI agent conversation.
Deflection rate: The percentage of customer contacts resolved by AI without requiring human agent involvement.
Deterministic governance: A system architecture where outputs are governed by explicit, predictable rules rather than probabilistic model inference.
First-contact resolution (FCR): The percentage of customer interactions resolved completely on the first contact without escalation or callback.
Hypercare: A defined post-deployment support phase, typically three months, where dedicated resources monitor system stability and resolve issues with high urgency.
Human-in-the-loop: An architecture where human judgment is an active, designed layer in AI-driven workflows rather than a passive fallback triggered only by AI failure.
RAG (Retrieval-Augmented Generation): An AI approach that retrieves relevant documents from a knowledge base and feeds them to a language model as context, improving factual accuracy but without guaranteeing policy compliance or the audit trails EU AI Act requires.
SOC 2 Type II: An auditing standard verifying that a vendor's security controls have operated effectively over a defined period (typically six to twelve months), not just at a point in time.