Blits.ai vs Kore.ai (2026)

Agents act in outside systems through three routes. A catalog covers 56 enterprise systems, among them Salesforce, SAP, ServiceNow, Workday, Zendesk, Oracle, Jira, Snowflake and Databricks, and business tools for HubSpot, Freshdesk, Zoho, Notion, Odoo, Dynamics 365 and Microsoft 365 connect through per system OAuth.

Custom functions call REST or SQL sources, or run JavaScript in an isolated sandbox, with versioning and publishing. External MCP servers connect over SSE or HTTP, and their tools appear to the agent. Built in tools add web search, file generation, payment links, lead capture, handover, and call control on voice such as transfer, DTMF and callback scheduling.

Sourceblits.ai/featuresread 2026-10-13

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

A BPMN style canvas lays out each conversation as flows of dialog, action, condition and AI blocks, with subflows, functions and agents referenced from any flow and versions published separately to Test and Live.

The agent block hands a turn to an LLM agent, and the trigger workflow block starts an agentic workflow, an agent loop that calls tools on its own until the task is done. Workflows carry instructions, configuration, a validation check and an on or off switch.

Agentic tasks hold a deferred action of the do X when Y kind, with scheduled rechecks, an expiry and a dry run mode, so a step can wait days for its condition before it runs.

Sourceblits.ai/featuresread 2026-10-13

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

Fourteen channels carry work to an agent, from the web chat widget, WhatsApp, SMS and phone to Microsoft Teams, Slack, Telegram, Facebook Messenger, email, Alexa and Google Assistant, an in app webview, a digital human and a REST or WebSocket API.

Agentic workflows also start on a schedule, or when an outside system calls a per workflow token endpoint, which queues the run with the request body as its input and allows 30 runs a minute per workflow by default.

Agentic tasks recheck a condition on a schedule before acting, outbound campaigns dial a contact list inside a set calling window, and an incoming email webhook turns mail into conversations.

Sourceblits.ai/api-docsread 2026-10-13

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

Retrieval runs over a maintained document library that ingests PDF, Word, PowerPoint, Excel, CSV, JSON, Markdown, images and Outlook messages, crawls websites and their subpages on a recrawl, and keeps version history with revert.

Chunk size and overlap are configurable, storage sits on PostgreSQL pgvector or file based HNSW indexes, and search combines dense vectors with BM25 keyword matching and deduplication. Agents can also query registered PostgreSQL or SQLite databases directly.

Answers cite the document, page and source date, newer content ranks ahead when sources overlap, and retention rules expire outdated material automatically.

Sourceblits.ai/featuresread 2026-10-13

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

A conversation keeps its state across turns and page refreshes, and a client can reconnect to an existing conversation, load or delete one user's history, and push state variables such as a customer name or account number into the session.

Inside a flow, a get history block reads the transcript, and a user data provider turns verified balances, loyalty points and tiers into variables. Data removal and retention controls sit in the GDPR toolkit.

Nothing the agent learns is written to a store of its own for later conversations, so what it knows about a returning customer is the history and the variables the host system supplies.

Sourceblits.ai/api-docsread 2026-10-13

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. There are two stores. Session Meta Memory is managed by the system, and Custom Memory Stores are persistent, structured with JSON Schema, readable and writable from code tools or workflow nodes, and scoped either to one user across all of that user's sessions or to a single session, with retention policies setting automatic expiry. Shared stores pass structured context between agents.

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

Agentic actions above a configurable threshold pause for a person, who approves or rejects them before they run, and a tool execution policy limits which tools an autonomous agent may call at all. Agentic tasks offer a dry run mode, and an anomaly review queue flags unusual conversations for review and can push them into a test suite.

For purchases, authorize then capture holds a payment for later capture, with amount ceilings and velocity limits. Staff can watch live conversations and take over, phone calls included, and blocked topics are enforced deterministically, independent of the model's judgment.

Sourceblits.ai/featuresread 2026-10-13

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

Single sign on works with Microsoft Entra ID and Google accounts, with multi factor authentication on admin login, and role based access control offers more than 60 permissions, custom roles per tenant, roles per bot, and separate publish rights for Test and Production. Tenants are isolated down to every record and query, API keys are hashed and validated server side, and PII and card numbers are masked at the gateway before text reaches a model.

Certifications cover ISO 27001:2022, ISO 42001 and SOC 2 Type II, and the trust center also shows GDPR compliance, with policies and controls available on request. Payments run on an isolated service built to PCI DSS v4.0.1 Level 1, with tokenization at the payment provider and encrypted credentials, while PCI DSS certification itself is still in progress.

Sourceblits.ai/featuresread 2026-10-13

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

Every agent turn can be inspected with token counts and trace detail, including each tool and SQL call with its origin, arguments and latency, and per agent statistics track token use and cost. Agentic workflow runs keep a full audit trail per run with downloadable run data, and the platform records user audit logs and tamper evident event logs. Conversation logs open in a permission gated transcript viewer, live conversations can be watched as they happen, and outbound campaigns log every call attempt.

Sourceblits.ai/featuresread 2026-10-13

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

Three deployment routes are offered, a managed service, the customer's own AWS, Azure or Google Cloud account, and an on premises install in the customer's data center, including air gapped sites.

The managed cloud has regional endpoints in the EU, the US, the UAE, Qatar and Australia, each with the same API, and data sent to a regional endpoint stays in that region.

Deployment is Kubernetes native, a self contained Docker Compose stack covers local installs, Google models can be routed to EU or UAE regions through Vertex AI, and transcription can run on GPU servers that Blits.ai operates itself.

Sourceblits.ai/deployment-optionsread 2026-10-13

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

Create with AI turns a written use case into a working bot with agents, flows, guardrails, knowledge suggestions and a go live checklist, and a solution portfolio in the admin console groups curated use cases by sector with their capabilities and channels.

More than 45 rich card types cover flights, hotels, orders, loyalty points, card benefits, balances and charts, each with its own prompt instructions. Each agent is still assembled for the customer, and there is no gallery of prebuilt agents to switch on. The public AI Use Case Library of 282 use cases is research on deployments across the market, not a set of installable agents.

Sourceblits.ai/featuresread 2026-10-13

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

An admin chooses the model for each agent from more than 15 providers, including OpenAI, Azure OpenAI, Anthropic, Google Gemini and Gemma, Meta Llama, Mistral, Microsoft Phi, Qwen, DeepSeek, GPT OSS, Hugging Face, Humain, Fanar and OpenRouter, and can move an agent to another provider without rebuilding it. Any OpenAI compatible model connects with the customer's own key.

Regional routing sends traffic to EU or UAE regions, reasoning effort is set per agent, and models above a plan's level stay visible but disabled. Embeddings come from OpenAI, Google, Nomic or OpenRouter, and intent recognition can run on Microsoft CLU, Dialogflow, Watson, Wit.ai or an LLM.

Sourceblits.ai/featuresread 2026-10-13

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

A REST API and a WebSocket API drive the platform from outside, authenticated with bot, agent or workflow JWTs sent as bearer tokens to platform.blits.ai or a regional host.

Endpoints open a conversation, send text, speech or files, check whether it is active, erase a user's data and run intent analysis on a message, and the WebSocket adds streaming speech, history loading and state variables. Separate endpoints run an agent with streamed NDJSON output, start an agentic workflow and return metered interaction counts per tenant. Each hosted MCP server has its own URL and exposes an agent's tools to outside MCP clients over Streamable HTTP with an API key.

Sourceblits.ai/api-docsread 2026-10-13

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

Test suites run automated conversations against agents, bots and workflows through the real chat pipeline, and multi turn sets play out as one continuous session with notes per turn for the grader.

Grading uses deterministic rules or an LLM judge with a configurable prompt, a selectable model and knowledge base evidence, and run reports show the pass rate, per turn verdicts, grader reasoning and run history, with regrading. Test cases import from JSON.

Monitors run scheduled health checks against rule based or LLM expectations and alert by email or webhook, and separate checks compare speech to text providers on word error rate and test MCP tool discovery.

Sourceblits.ai/featuresread 2026-10-13

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 web browsing tool sits among the built in agent tools beside web search and Wikipedia, so an agent can open and read web pages while it works. UiPath is one of the 56 connectable systems, and the agentic product connects RPA tools, which lets an agent hand screen based work to a robot the customer already runs. There is no hosted browser or desktop session for the agent to drive itself. Real browser testing, which loads the live chat widget under simulated network and CPU conditions, is quality assurance on Blits.ai's own product.

Sourceblits.ai/featuresread 2026-10-13

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