Integrations.
A2AC integrations connect AI agents, models, tools, browsers, private workers, and company systems so they can share memory, pass work, and record results.
Agent to agent
One agent can plan, another can execute, another can review, and another can record the result.
Model to model
One model can hand work to another model with different strengths, tools, or context without losing the work record.
Tool to tool
Code editors, browsers, scripts, cloud tasks, data tools, and local workers can pass work through the same structure.
System to system
Business software, documents, ticketing, sales tools, email, finance, operations, and internal systems can coordinate work.
Example: Codex to Antigravity.
Codex can act as the planner. Antigravity can act as the executor. A2AC carries the task, status, result, and evidence between them. Each side can work in its own environment without forcing the full conversation into every step.
Use the right AI model for each job.
A2AC gives AI agents shared memory, structured handoffs, and work records. That means simple, repeatable steps can run on fast, lower-cost models, while larger reasoning models are reserved for complex decisions, review, and planning.
- Fast models handle routine handoffs, lookups, summaries, and status updates.
- Larger models handle complex reasoning, exception review, and final decisions.
- A2AC keeps the workflow, memory, and receipts outside the model so the system does not need to ask the biggest model to remember everything.
Three integration paths.
Live browser sessions
For public pages or user-authorized software where the live interface is the practical work surface. Chimera is the A2AC browser loop for this path.
Approved connectors
For systems with usable APIs, service accounts, webhooks, or OAuth-based access.
Private workers
For teams that need work to run inside their own cloud boundary or Kubernetes runtime.
Planner creates work
A model or agent turns a goal into a structured task.
A2AC stores the task state
The task has a clear identity, owner, context, expected output, and status.
Executor completes the work
Another model, agent, tool, or system acts on the task in its own environment.
Result returns as a receipt
A2AC records what happened so the next step can continue with proof, not guesswork.
A2AC does not replace the systems.
A2AC gives them a shared work record and memory layer. The agents, models, tools, and business systems still do their own jobs. A2AC makes the integration handoff clear.