Search for evidence.
Ask for a supported conclusion.
Searchable documents, episodes, claims and subject knowledge give agents and people a shared basis for the next question. Answers carry their evidence with them.
The shared intelligence control plane for AI agents
Alveary
Every agent informed.
Every action visible.
Turn agent activity into durable memory, coordinate work across systems, and give people a complete operational view.
From activity to shared intelligenceFrom isolated sessions to shared intelligence
The session ends.
The knowledge shouldn't.
Claude Code in one terminal. Codex in another. Headless jobs in containers. Each session learns something, but the knowledge too often disappears when the window closes.
Alveary turns that experience into knowledge the organisation can keep and reuse. Agents retain their own models, tools, permissions and workspaces. What they gain is a common memory, a common queue and a common picture.
- 01
Connect
One install command per host. Claude Code, Codex and Gemini use the same collector and event contract.
- 02
Capture
Session hooks capture activity without making the agent wait on the network. Events, token usage and telemetry are normalised, stored and shown live.
- 03
Remember
Experience becomes searchable documents, episodes, claims and subject knowledge. Search returns evidence; Ask adds a conclusion with the evidence attached.
- 04
Act & govern
Route the next prompt and the next model job. Policies, reviews and admissions keep people in control of what is remembered and done.
01 / Remember collectively
What one agent learns,
the next can use.
Improving agents does not have to mean changing the model, the prompt or the tools. Alveary adds a persistent knowledge layer between what agents experience and what they do next.
Experience, recorded
Finished work becomes an episode with cited evidence, preserving what happened and what supports it.
Knowledge, reviewed
Episodes yield claims. Review and consolidation turn those claims into cumulative knowledge and reusable know-how.
Context, carried forward
Every session starts with a recall pack carrying what earlier work learned, so the next agent has more than an empty window.
Search for evidence.
Publish once.
Start aligned.
Publish a standard, skill or agent once and every fresh install boots with it. Shared knowledge can inform both the organisation's understanding and its executable behaviour.
02 / Act coherently
The right work.
The right system.
Work does not run in one place. Alveary makes two distinct routing decisions: where a prompt goes, and which model deployment serves a job. Both decisions are recorded.
Alveary Dispatch
Prompts go to agents.
Deliver work to an attended terminal a person owns or to a headless container running Claude Code or Codex.
Autonomous loops use those same paths to triage TODOs, take small items through to a pull request, and merge green ones inside policy. People do not have to relay every prompt.
Governed model routing
Jobs go to models.
From composing an episode to answering a question, each capability uses a deployment admitted on evaluation evidence.
An administrator defines which deployments may serve a capability and how far its data may travel. An operator chooses among those deployments, so work that must stay local stays local.
03 / See completely
Ask what happened.
See what it rests on.
The home page opens as a conversation. Ask what your agents did today and Alveary answers from what it remembers, with every sentence cited, or tells you it cannot.
Behind the conversation is one live dashboard for the work, its cost and the health of the collection feeding it.
- Sessions & timelines
- Follow activity across agents and runtimes in one operational view.
- Analytics & token cost
- Understand usage and cost with explicit provenance.
- Collector health
- See the state of the telemetry collection that feeds the dashboard.
- Session summaries
- See what the work produced without reconstructing every session yourself.
04 / Govern deliberately
Control is part
of the work.
Every consequential action stays reviewable. Deliberate controls govern access, remembered knowledge, model choice and telemetry.
- Access, scoped
- Role-based access with per-feature and per-project grants, declared in one reviewable matrix.
- Knowledge, checked
- Review queues for machine-proposed knowledge before it is consolidated.
- Models, admitted on evidence
- Admissions expire when what would run no longer matches what was measured.
- Telemetry, governed by project
- Per-project policies determine redaction and sampling.
- Erasure, verified again
- Erasure is re-validated after it completes.
- Change, recorded
- An audit row for every state change, alongside recorded routing decisions.
Why “Alveary”?
An alveary is a beehive and, since the sixteenth century, a repository of knowledge to which many contributors add. The name brings together independent contributors working through one structure and durable knowledge created from their combined activity.
Alveary, explained
A few useful
distinctions.
What is Alveary?
Alveary is a shared intelligence control plane for AI agents. It turns agent activity into persistent knowledge, coordinates work across systems and gives people an operational view of what agents remember and do.
Which AI agents can connect?
Claude Code, Codex and Gemini connect through the same collector and event contract. Prompt dispatch supports attended terminals and headless containers running Claude Code or Codex. Telemetry support and dispatch support are distinct capabilities.
Does Alveary replace an agent's model, tools or permissions?
No. Agents keep their own models, tools, permissions and workspaces. Alveary adds a common memory, a common queue and a common operational picture.
Can model work stay on a local system?
Administrators set which deployments may serve a capability and how far its data may travel. Local models are reached through an outbound-only relay on a device you own. The relay refuses endpoints outside the local network; hosted models are accessed online.
How does Alveary support evidence-based answers?
Search returns evidence. Ask returns a conclusion with supporting evidence attached. The conversational home page answers questions about agent activity with every sentence cited, or states that it cannot answer.
What keeps autonomous work under human control?
Access grants, workspace routes, runner capabilities, schedules and policy constrain the work. Review queues govern machine-proposed knowledge, model admissions rely on evaluation evidence, and state changes and routing decisions are recorded.
Alveary
Better agents.
Because the organisation remembers.
Not because the model changed. Remember collectively. Act coherently. See completely. Govern deliberately.