Kore.ai vs Maven AGI (2026)

The tools and integrations layer connects outside systems over HTTP, MCP, AWS Lambda, SDK invocations, the A2A protocol, sandboxed tools, OpenAPI definitions and webhooks, with circuit breakers and retry logic. Data sources such as Salesforce, SAP, Microsoft, VMware and Zendesk are reached through tool definitions, so none of them is built into agent logic.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

More than a hundred out of the box integrations span help desk, CRM and telephony, including Salesforce, Zendesk, Freshdesk, Genesys, Twilio and Slack, and unify them into one layer without data migration. The agent takes action in these systems, validating identity, checking transaction status, updating records and issuing refunds or replacements end to end across the customer's existing help desk, CRM and telephony.

Sourcemavenagi.comread 2026-09-05

Four orchestration patterns are invoked through ABL constructs and enforced by the runtime. They include parallel fan out with barrier synchronization, asynchronous delegation that survives pod restarts, and turns carrying several intents that map to several agents.

Supervisor routing arbitrates handoff and delegation, a pipeline classifier skips the reasoning loop for intents it recognizes with high confidence, and the Google A2A protocol carries communication to agents hosted on other runtimes. The runtime's two engines execute agentic reasoning and deterministic flows in parallel from one definition.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

A single reasoning engine works through multi step requests, takes actions in the customer's tools and adapts to edge cases, with no decision trees to maintain. It resolves an inquiry end to end by validating identity, checking transaction status, updating records and issuing a refund or replacement without escalation. The platform is organized as agents that each own a specific outcome across the customer journey, and escalation hands a person a prefilled brief.

Sourcemavenagi.com/product/agent-platformread 2026-09-05

Agents deploy across 27 channels covering web, voice, messaging, email and enterprise APIs, among them Microsoft Teams, ServiceNow, Slack, Webex and Zoom, with consistent behavior and no rebuilding for each channel.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Customers reach the agent across text, email, web, voice and in app channels, with voice to voice agents and telephony driven through the customer's existing stack. Maven also acts proactively, anticipating and resolving issues before a customer reports them.

Sourcemavenagi.com/product/agent-platformread 2026-09-05

Search AI is the platform's knowledge retrieval layer, providing RAG, vector search, knowledge graph traversal and reranking to surface context to agents as they reason. The customer configures it and the platform maintains it, and the retrieval pipeline comes with guidance on model selection, chunking and context window sizing.

SourceKore.ai Agent Platform and Search AI documentation; docs.kore.ai/agent-platformread 2026-09-05

Answers are grounded in what Maven calls governed knowledge, drawn from live, version accurate knowledge bases through retrieval augmented generation. The platform unifies knowledge and personalization sources across fragmented systems into one layer. Maven describes no index, embeddings layer, retrieval architecture, refresh behavior or source level citation.

Sourcemavenagi.com/product/agent-platformread 2026-09-05

Memory is a formal construct in Agent Blueprint Language alongside goals, tools and guardrails, executed against a shared memory layer that coordinates state across both cognitive engines. Session Meta Memory is managed by the system.

Custom Memory Stores are persistent and structured with JSON Schema, and each takes a scope: one user across all of that user's sessions, a single session that clears when it ends, or the whole application. A retention policy sets each store to last a session, a day, a week or a month, and tools read, write and delete store content at run time. Shared stores pass structured context between agents.

Sourcedocs.kore.ai agent platform memory stores guideread 2026-10-25

The platform integrates personalization sources and draws on prior interactions to answer in context, and Maven says it learns from every conversation. That works through retrieval over connected systems and model improvement. The agent keeps no state of its own that a customer can scope or delete. The persistent stores the product keeps are its knowledge bases.

Sourcemavenagi.com/product/agent-platformread 2026-09-05

The Workflow Engine runs workflows lasting minutes to days, with built in steps where a person reviews the work and approval gates that hold it until someone signs off. Guardrails are a core ABL construct enforced outside the model, the runtime carries guardrails in several tiers with circuit breakers and rate limits, and supervisor routing checks those guardrails, so the runtime overrides an agent's chosen route when policy requires. Arch AI hands its work products to a person to review and never applies them silently.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Defined guardrails and governed knowledge are what Maven says make autonomous resolution reliable at enterprise scale, alongside AI powered support for humans in the loop. When a case goes beyond its capabilities, the agent escalates with a prefilled brief carrying customer history, attempted resolutions and a confidence score, so the person does not start from zero. Maven names its guardrails but does not describe them. There is no approval surface before sending, no sign off step and no policy threshold that holds a sensitive action for a person.

Sourcemavenagi.comread 2026-09-05

Security and governance include SSO, role based access control, guardrails, audit logging, encryption, key management, multitenancy with full tenant isolation, PII detection, identity verification and compliance reporting, on an enterprise foundation carrying PCI, SOC 2, GDPR and FedRAMP certification, with handling for the right to erasure and data minimization controls.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Maven is certified for SOC 2 Type II, ISO 27001:2022, PCI DSS v4.0 Level 1 and HIPAA/HITECH. A trust center sits at trust.mavenagi.com, and a Data Processing Agreement carries standard contractual clauses, technical and organizational measures and subprocessor lists.

Access controls include single sign on and directory provisioning through Okta, Entra and SAML, role based access, two factor authentication for agents and admins and secure salted credential storage. Audit logs cover account, user and app changes, business rules, ticket deletions and settings. ISO 27701, 42001, 27017 and 27018 certifications and GDPR, CCPA and CPRA attestations are expected but not yet held.

Sourcemavenagi.com/legal-privacyread 2026-09-05

Observability follows the agent's reasoning, capturing every reasoning step, tool call, routing decision and guardrail evaluation as structured trace data across chains of delegated agents. Deep Observability records over 200 trace events per session and provides runtime inspection, alerting and debugging of each session across interactions between agents.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Account level audit logging covers account changes, user changes, app changes, business rules, ticket deletions and settings. A real time dashboard tracks every deflection, escalation and cost saved, with resolution, deflection and NPS metrics. There is no step by step decision trace, tool call log or retrieval record for an individual agent action.

Sourcedocs.mavenagi.com/security-and-privacy-detailsread 2026-09-05

Kore.ai runs as cloud SaaS or self hosted for organizations with data residency or compliance requirements, with the same platform capabilities either way. The infrastructure layer plugs into Microsoft Azure, Google Cloud, AWS, on premises installations and private VPC deployments, so the customer chooses where the platform runs.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Service data is hosted primarily in Azure, Google and AWS data centers, with no regions named and no region selection. Maven also claims multi region and multi language deployments for enterprises but lists none. The Data Processing Agreement relies on standard contractual clauses for international transfers. There is no VPC, single tenant or self hosted option.

Sourcedocs.mavenagi.com/security-and-privacy-detailsread 2026-09-05

The AI agent marketplace offers named prebuilt agents across banking (ATM Locator, Dispute and Fraud Handler), retail (Product Search, Order Management), HR (Absence Management, Screening Agent), IT (Incident Management, Access Management), sales (RFP Response Automation, Lead Analyzer), legal and finance (Contract Amendment Assistant) and customer service (Escalation Predictor, Contextual Email Responder), installed with one click. Marketplace agents import with their predefined tools and workflows, and any component can be edited afterwards.

SourceKore.ai AI agent marketplace and Create an Agent from Marketplace documentation; docs.kore.ai/agent-platform/agents/create-from-marketplaceread 2026-09-30

Customers assemble agents themselves, with a no code AI Agent Designer for building and fine tuning agents, more than a hundred out of the box integrations and a platform framed as one agent per outcome across the customer journey. There is no prebuilt agent catalog, template gallery or marketplace of packaged agents to browse and select. The named agents are outcomes the platform covers, not assets a customer adopts as units.

Sourcemavenagi.comread 2026-09-05

Every layer of the ecosystem is pluggable, so the customer swaps providers without rewriting agents. Supported models include OpenAI, Anthropic, NVIDIA, Cohere and LLaMA 2 among others, and open source and custom fine tuned models work through the same interface. A models library lets an admin add a new model or a custom integration, models hosted by a third party or by the enterprise itself can be brought in, each feature can be assigned its own model, and an analytics dashboard reports token distribution, latency and cost per model connection.

SourceKore.ai Agent Platform and Generative AI model documentation; docs.kore.ai/agent-platformread 2026-09-05

Customers get no model selection, routing or way to bring their own model. Maven calls itself an intelligence layer between enterprise data, models and teams but does not say which models it uses, and it names no provider. The subprocessor list sits inside the DPA on the trust center, not on a public page.

Sourcemavenagi.com/product/agent-platformread 2026-09-05

Agent Blueprint Language is a typed language driven by schemas, compiled from DSL to intermediate representation to runtime, with definitions stored in Git as diffable YAML, reviewed in pull requests and promoted by the same CI/CD practices as application code. The tools layer exposes HTTP, MCP, SDK invocations, OpenAPI, webhooks and the A2A protocol for communication with agents on other runtimes, including agents hosted outside the platform.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

A developer platform opens Maven's own product to outside builders. SDKs in Node.js, Python, Java, Ruby, PHP and Swift sit alongside the APIs, on a dedicated developer site at developers.mavenagi.com. Teams build extensions against an extension model that spans conversational surfaces, copilots, knowledge, personalization, actions and data movement.

Sourcemavenagi.com/resources/platform-launchread 2026-09-05

Agent Evals is an automated evaluation framework for the customer's own agents, with test suites, regression tracking and quality gates that run as part of the deployment pipeline, and the results decide whether an agent is promoted. The environment pipeline routes every agent through a staging tier where automated suites and regression checks run before a pilot tier validates behavior on limited real traffic. Validation at compile time catches broken references before deployment.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

A no code AI Agent Designer lets teams build, fine tune, test and monitor agent behavior before and after going live. In the Agent Designer, CX teams tune agent behavior, test changes against real scenarios and validate them before a customer ever sees them. A real time dashboard tracks deflection, escalation, resolution and NPS, so a change can be judged against outcomes. Maven does not describe a scored evaluation behind the testing.

Sourcemavenagi.comread 2026-10-07

Agents reach outside systems only through the tools layer (HTTP, MCP, Lambda, SDK, A2A, OpenAPI and webhooks) and reach users through 27 programmatic channels. They do not drive a browser, a virtual desktop or software that lacks a programmatic interface.

SourceKore.ai Agent Platform documentation; docs.kore.ai/agent-platformread 2026-09-05

Actions go through Maven's integration layer and published APIs into help desk, CRM and telephony systems. There is no browser control, hosted session, virtual desktop or computer use.

Sourcemavenagi.com/product/agent-platformread 2026-09-05