COMPARISON WITHOUT BLANKET SCORES
Compare AI ecosystems side by side
Which agents belong to which AI provider? Compare two ecosystems using original agent profiles, documented products and explicit evidence limitations.
linked AI ecosystems
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Source status: October 8, 2026. Individual agent controls remain Unknown without suitable field-level evidence.
How we compare
A fair comparison starts with a real workflow and an explicit deployment plan. Count each linked agent once, preserve the distinction between an API and an end-user agent, and verify documentation at the product level. The comparison does not assign an overall winner or numerical security score. Controls that have not been publicly documented remain Unknown.
Seven ecosystems in one matrix
The table reflects catalogued products. It does not imply that each provider offers every category at every plan.
| Provider | Agent examples | Catalogued roles | Entries | Documentation |
|---|---|---|---|---|
| OpenAI | ChatGPT Deep Research, OpenAI Codex, ChatGPT Work, ChatGPT dots | Automatisierung, Coding, Enterprise & Produktivität, Recherche | 6 | Source ↗ |
| Anthropic | Claude Research, Claude Code, Claude Cowork | Coding, Enterprise & Produktivität, Recherche | 3 | Source ↗ |
| Gemini Deep Research, Google Jules, Gemini Spark, NotebookLM Deep Research | Automatisierung, Coding, Recherche | 7 | Source ↗ | |
| Google Cloud | Gemini Enterprise Agent Platform, Gemini Agent, Google AlphaEvolve | Coding, Enterprise & Produktivität | 3 | Source ↗ |
| Microsoft | Microsoft Copilot Researcher, Microsoft Copilot Studio, Microsoft Copilot Analyst, Microsoft Foundry Agent Service | Analyse, Automatisierung, Recherche, Security & IT, Service, Sales & Daten | 7 | Source ↗ |
| Perplexity | Perplexity Deep Research, Perplexity Computer, Perplexity Comet Assistant | Automatisierung, Produktivität, Recherche | 3 | Source ↗ |
| Meta | Meta Muse | Produktivität | 1 | Source ↗ |
Practical comparisons
| Scenario | What matters |
|---|---|
| OpenAI versus Anthropic for coding | Compare Codex and Claude Code in their actual runtimes: repository access, local or hosted execution and review gates differ. Do not compare model families as if they were deployable products. |
| Google/Gemini versus Perplexity for research | Separate research agents, personal assistants, cloud infrastructure and browser functions. Source traceability, actions and credentials depend on the chosen product and plan. |
| Microsoft versus Google Cloud for enterprise | Start with identities, logging, deployment regions, connector permissions and approval workflows. Platform policies do not automatically document each agent. |
Evaluation checklist
Scope and documentation are more informative than a generic rating.
| Control family | Review focus |
|---|---|
| Autonomy / tools | Exact agent runtime, permissions, external actions and approvals |
| Security & audit | Isolation, logs, retention, export and independent validation |
| Privacy & EU | Data purpose, contract, residency, transfer and precise plan scope |
| Availability | GA versus preview, geographical access, subscription and APIs |
Frequently asked questions
Is the ecosystem with most agents the best?
No. Catalogued profile count is coverage, not deployment suitability, quality or security.
Can I compare three ecosystems?
Yes. Select two or three providers; displayed products are taken from the shared catalog.
How are GDPR and data residency compared?
At the level of a concrete service, deployment and plan. Missing documentation is Unknown, not No.
Are all security controls independently tested?
No. Most published source records document supplier assertions. An independent test must be separately identified.
What this comparison can tell you
It surfaces documented product roles: research, coding, work agents, enterprise platforms and specialist tools. All linked profiles come from one AgentenCode dataset. Control counts describe documentation coverage, not pass rates.
Where comparability ends
OpenAI Codex versus Claude Code is a close coding comparison. Scoring an entire enterprise platform against a single browser assistant would be methodologically misleading. Ecosystem comparison therefore distinguishes roles and links to a separate direct agent comparison.
Compare individual agents →Ecosystem comparison FAQs
What does the ecosystem comparison measure?
Documented agent profiles, product types and available evidence, not general model intelligence.
Does more trust evidence imply better security?
No. Evidence coverage measures documentation, not an independent certification.
Can all these products be directly compared?
Only within meaningful use and runtime contexts. Platforms, agents and models remain distinct.
Choose by workflow: three defensible decision paths
A useful ecosystem comparison starts with the task and its exposure, not the largest provider name. First, consider software development: teams comparing OpenAI Codex, Claude Code and Gemini CLI should document the exact runtime they intend to use. Repository and shell access, approvals for file changes, handling of credentials and patch-review mechanisms matter. A documented CLI capability does not establish equivalent support in a cloud agent.
Second, for research and knowledge work, compare products such as ChatGPT Deep Research and Perplexity Deep Research on source traceability, access to private documents, citation accuracy, freshness and data handling. A fluent research answer may still lack sufficient evidence. Third, for enterprise workflows, controlled connectors, identity propagation, audit events and failed-action recovery may be more important than the number of catalogued agents.
A repeatable evaluation workflow
- Define the same task: Fix a bounded, permitted outcome and consistent inputs for every candidate.
- Pin product scope: Record the client, plan, region, enabled tools and granted permissions; otherwise unlike variants may be compared.
- Specify action controls: Determine when human approval is required, how execution can be interrupted and how results can be rolled back or audited.
- Run documented cases: Capture task completion, human corrections, failures, elapsed time and total per-task costs.
- Separate evidence types: Distinguish vendor documentation, reproducible pilot findings and unknown properties. One successful test is not a general security assurance.
Document the decision, not a winner
Keep a brief record for each shortlisted agent: product version, review date, primary-source link, effective permissions, observed actions, outstanding unknowns and accountable owner. When personal data is involved, map the actual processing chain, including processors and sub-processors, data locations, retention and deletion. EU data residency, a signed DPA and overall GDPR compliance are distinct claims. None follows automatically from belonging to an ecosystem.
Read the methodology for AgentenCode's field-level evidence rules, use Agent Check for documented requirements, and open the individual agent comparison for a narrower functional assessment. This matrix is a decision aid, not a league table.