Kendr resources for research-to-execution teams
Evergreen guides for teams comparing AI research tools, building agentic workflows, publishing MCP skill packs, and turning everyday work into reusable knowledge.
Kendr AI vs Gemini Deep Research vs ChatGPT Deep Research
Compare private research workspaces, Google-source research, ChatGPT reports, citations, governance, artifacts, and scheduled follow-ups.
What makes a deep research workspace useful?
How evidence, source trails, reusable context, and final deliverables fit together when the answer has to be trusted.
How teams should design agentic workflows
A practical model for routing, approvals, execution lanes, artifacts, and human control in multi-step AI work.
Build an AI knowledge base from ongoing work
Why the best knowledge base is created from completed research, decisions, prompts, sources, and project memory.
MCP skill packs for governed AI tools
How skill packs make agent capabilities discoverable, installable, and safer for teams that need repeatable execution.
Competitive intelligence AI workflows
Turn market signals, competitor claims, source trails, and internal observations into decision-ready briefs.
Vendor evaluation AI checklist
Compare products, review risk, preserve decision trails, and keep vendor research reusable for renewals.
Architecture review with an AI agent
Analyze repositories, surface technical risk, and turn architecture findings into phased engineering plans.
Cloud operations AI assistants
Inspect cloud systems, diagnose issues, and prepare approval-gated remediation across operational workflows.
CI/CD failure triage with AI
Use logs, recent changes, and test output to find likely causes and prepare safe fixes.
Model Context Protocol explained
Learn MCP hosts, clients, servers, tools, resources, prompts, transports, and approval-aware use.
RAG explained
Understand retrieval augmented generation, embeddings, chunking, vector search, citations, and evaluation.
A2A agent-to-agent protocol explained
Learn agent discovery, tasks, messages, artifacts, interoperability, and delegation patterns.
ACP agent control explained
Understand control layers, admission checks, policy, approval, governance, and safe execution.
Agentic AI systems explained
Learn how goals, planning, tools, memory, workflows, approvals, and evaluation fit together.
AI agent protocols compared
Compare MCP, RAG, A2A, ACP, and agentic AI concepts in one practical overview.
Why these guides exist
Kendr sits in a category that many teams are still learning to name. It is not only a chatbot, not only a document search product, and not only an automation runner. It is a workspace for moving from research to execution while keeping evidence, context, approvals, and reusable knowledge connected.
That means the best educational content has to explain the operating model, not just the feature list. Teams want to know how deep research differs from search, how agentic workflows can remain controlled, how knowledge bases should be built from real work, and how MCP skill packs make agent capabilities discoverable and safer to reuse.
This resource library is designed around those questions. Each guide is evergreen, practical, and linked back to the product surfaces where the idea becomes usable inside Kendr.
Recommended reading path
- Start with deep research: learn how Kendr keeps questions, evidence, sources, and outputs connected.
- Move into agentic workflows: see how research and context turn into plans, approvals, and reviewable work.
- Build the knowledge base: preserve the useful findings, decisions, and project memory created by the work.
- Add MCP skill packs: package repeatable capabilities so agents can use tools with clearer instructions and boundaries.
The sequence matters because these ideas reinforce each other. Research creates context. Context improves workflows. Workflows create artifacts. Artifacts and decisions become knowledge. Skill packs make the workflow repeatable.
Topics Kendr covers
The current guides focus on the highest-intent topics for teams evaluating AI workspaces: deep research workspace, agentic workflow software, AI knowledge base, MCP tools, skill packs, governed AI execution, and research-to-execution workflows.
Future pages can extend this cluster into more specific search surfaces such as competitive intelligence workflows, vendor evaluation templates, architecture review prompts, cloud operations assistants, Terraform review workflows, Kubernetes triage, policy review workflows, and searchable literature review systems.