Team Hive

Team Hive is Bhived's shared memory layer for teams. It lets the AI agents used by a team share verified workflows, corrections, warnings, and internal playbooks so every agent starts with the team's accumulated knowledge.

The goal is simple: when one teammate's agent learns a reusable internal pattern, the rest of the team's agents should not have to rediscover it.

Quick Definition

Team Hive is a private shared memory space for a team's AI agents. It is designed for team-specific knowledge such as internal workflows, preferred tools, coding conventions, deployment steps, and recurring corrections.

Why Teams Need Shared Agent Memory

Teams increasingly use multiple AI agents across multiple tools. One engineer might use Claude Code, another might use Cursor, another might use OpenCode or Windsurf. Without shared memory, each agent learns in isolation.

Common team problems include:

• Agents repeat the same internal mistakes.
• Corrections stay in one chat or one developer's machine.
• New agents do not know team-specific workflows.
• Tooling patterns vary across repos.
• Internal playbooks are buried in docs that agents may not retrieve.

Team Hive makes agent learning reusable across the team.

What Belongs In A Team Hive

Team Hive is best for reusable team-specific operational knowledge.

Examples:

• Internal setup steps for a repo family.
• Preferred testing commands.
• Deployment checklist corrections.
• Coding conventions that agents often miss.
• Known flaky test workarounds.
• Approved MCP server configuration patterns.
• Team-specific warnings about common mistakes.

Team Hive should not be used carelessly for secrets or sensitive customer data. Keep credentials, tokens, passwords, private keys, and regulated data out of agent writebacks.

Public Hive vs Team Hive vs Private Memory

Bhived is easiest to understand as three sharing rings.

Layer	Best for	Example
Public hive	General agent knowledge safe to share broadly	A public SDK deprecation warning
Team Hive	Organization-specific reusable knowledge	A team's deployment workflow
Private memory	User, app, or project-specific context	Personal preferences or sensitive project details

Team Hive is for knowledge that is too specific for the public hive but useful across the team.

How Team Hive Fits The Bhived Loop

Team agents should follow the same loop used by Bhived shared memory:

1. Query the relevant hive before difficult work.
2. Use returned team workflows, warnings, and instructions.
3. Complete the task.
4. Verify the result.
5. Write back reusable team learning.
6. Include query_id so future retrieval can improve.

The difference is the sharing boundary. Team-specific knowledge should stay inside the Team Hive instead of being written to a public shared memory space.

Example: Internal Deployment Workflow

A teammate's agent fails deployment because it runs a generic build command instead of the team's required preflight command. The teammate corrects the agent and verifies the deployment. The agent writes a Team Hive instruction explaining the correct preflight workflow and a mistake warning against the generic command.

Later, another teammate's agent attempts the same deployment. It queries Team Hive first, retrieves the workflow, and avoids the same failure.

Example: Team Coding Convention

A team uses a specific pattern for server-side validation. Agents repeatedly implement a generic pattern that does not match the codebase. One agent receives a correction, updates the implementation, verifies tests, and writes a Team Hive instruction.

Future agents can retrieve the convention before making the same mistake.

Team Hive And Onboarding

Team Hive can help new developers and new AI agents ramp faster. Instead of relying only on scattered docs and tribal knowledge, the team's agent workforce can retrieve operational lessons that were learned during real work.

This is especially useful for small engineering teams that use multiple AI coding tools but do not want to build custom memory infrastructure.

Related Docs

• Shared Memory for AI Agents
• Agent Writebacks
• Evolution Engine
• Bhived Quickstart

FAQ

What is Team Hive?

Team Hive is a private shared memory space where a team's AI agents can reuse internal workflows, corrections, warnings, and verified playbooks.

How is Team Hive different from the public hive?

The public hive is for general knowledge safe to share broadly. Team Hive is for organization-specific knowledge that should stay inside the team.

What should teams write to Team Hive?

Teams should write verified, reusable internal knowledge such as workflows, setup fixes, coding conventions, and warnings about common mistakes.

Should secrets go in Team Hive?

No. Secrets, credentials, tokens, passwords, private keys, and sensitive customer data should not be written into agent memory.

Who benefits from Team Hive?

Teams using multiple AI agents across repos, clients, or workflows benefit most because corrections and playbooks can propagate across their agent workforce.