Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A
How a Python agent and a Go agent collaborate on contract compliance using the Agent2Agent protocolYour contract compliance pipeline has a problem. The data science team wrote a brilliant extraction agent in Python. It uses Gemini to parse legal contracts and pull out every key term.
- ▪How a Python agent and a Go agent collaborate on contract compliance using the Agent2Agent protocolYour contract compliance pipeline has a problem.
- ▪The data science team wrote a brilliant extraction agent in Python.
- ▪It uses Gemini to parse legal contracts and pull out every key term.
Google Developers Blog files mainly under programming. We currently carry 20 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Google Developers Blog |
| Canonical URL | https://developers.googleblog.com/build-cross-language-multi-agent-team-with-google-agent-development-kit-and-a2a/ |
| Publication time | Not provided by source |
| Retrieval time | 2026-07-25T23:18:53.148Z |
| Last seen | 2026-07-25T23:19:03.106Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | -c5cTmNFiS5J |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
How a Python agent and a Go agent collaborate on contract compliance using the Agent2Agent protocolYour contract compliance pipeline has a problem. The data science team wrote a brilliant extraction agent in Python. It uses Gemini to parse legal contracts and pull out every key term. The security engineering team built a blazing-fast compliance validator in Go, using all deterministic logic and no LLM. Both work perfectly in isolation. But now you need them to work together, as a single coordinated pipeline, and neither team is willing to rewrite their service in another language.This is the reality of production AI systems: different teams, different languages, different deployment targets.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Google Developers Blog.
Discussion
0 commentsMore from Google Developers Blog
