One Interface, Multiple Agents: Cross-Agent Workflows with Claude, Codex, Antigravity, and Grok

TL;DR: Operating multiple agents from one interface first solves tool-switching and context-carrying friction. At a deeper level, it lets users select the right agent for each task scenario, then have Claude, Codex, Antigravity, and Grok complete the work together. The model page narrows the selection; the unified interface handles dispatch and handoff; and humans retain authority over permissions, evidence, and final judgment.

My working environment can now operate multiple agents from a single interface. The experience is remarkable, like the legendary Guo Jing from The Legend of the Condor Heroes, who somehow mastered every martial art tradition at once: the Eighteen Dragon-Subduing Palms, the Seventy-Two Styles of Kongming Fist, Ambidextrous Combat, the Sinew-Forging Chapter, the Nine Yin Manual’s overview and healing sections, the Heavenward Ladder, Snake-Glide and Fox-Roll, the Floating Silk Force, Bone-Shrinking Arts, the Soul-Moving Technique, the Great Demon-Subduing Fist, Five-String Hand, the Tendon-Crushing Divine Claw, the White Python Whip, Complete Perfection internal cultivation, the Golden Swallow Skill, the Heaven’s Net Formation, Southern Mountain Boxing, Mountain-Opening Palm, Finger-Dislocation techniques, the Empty-Hand Fist, the Sword Art of the Yue Maiden, the Demon-Subduing Staff, the Golden Dragon Whip, Southern Mountain Saber, the Huyan Spear, the Iron Shirt and Golden Bell techniques, Jebe’s divine archery, Mongolian wrestling, Carefree Wandering, Esoteric Formations, and the Military Treatise of Yue Fei, all of it.

Claude can operate Codex, and Codex can operate Claude. Extend that further, and Antigravity and Grok can be brought into the picture as well.

On the surface, this looks like having a few more tool options. In practice, it changes the fundamental way we work with AI.

Working with multiple AI tools used to mean handling all the friction yourself:

Figure 1 | How tool-switching fragments the work

flowchart LR
    A[Think of a question] --> B[Open the first tool]
    B --> C[Copy the content]
    C --> D[Switch to a second tool]
    D --> E[Re-explain the context]
    E --> F[Switch to yet another tool]
    F --> G[Consolidate multiple outputs]
    G --> H[Return to the original work]

Every tool might be excellent on its own, but the user still has to carry content between them, maintain context, track progress, and decide which output to pass to the next tool.

Now those agents can share the same operational surface.

Figure 2 | A single interface places multiple agents in the same working context

flowchart TB
    U[User]
    I[Unified Interface]
    C[Claude]
    X[Codex]
    A[Antigravity]
    G[Grok]
    U --> I
    I --> C
    I --> X
    I --> A
    I --> G

Fewer open windows is just the visible result.

What actually shifts is the user’s orientation: from “using one AI” to “coordinating a set of agents that can work together.”

Real Convenience Means Work Stays Whole

Having multiple AI tools available is, in principle, freedom of choice. It can also become an operational burden.

Each additional tool can mean a new interface, a different interaction pattern, a separate context, and another output format. Workers appear to gain more capability while spending more time managing the tools themselves.

There are at least four kinds of friction here.

The first is interface friction. Users constantly open, close, and switch between windows. These actions produce nothing, but they interrupt thought.

The second is context friction. Handing the same task to a different agent often requires re-explaining background, objectives, constraints, and current progress. Any missing detail can send the next agent off on the wrong premise.

The third is workflow friction. Users have to remember which agent finished what, who should receive the next step, which outputs have been confirmed, and which are still provisional.

The fourth is accountability friction. When results are scattered across different tools, it becomes difficult to trace: which agent made which decision? Which content is source material? Which is inference? Which results were verified by a human?

The value of a unified interface, then, begins with reducing that tool friction, keeping work from being carved up by the tools themselves.

From “One AI Answers” to “A Working Network”

The traditional pattern of AI use looks roughly like this:

Figure 3 | The conventional question-and-answer mode

flowchart LR
    H[Person asks a question] --> M[One AI produces an answer]
    M --> J[Person decides whether to use it]

A cross-agent workflow looks more like this:

Figure 4 | The basic structure of a cross-agent working network

flowchart TB
    H[Person defines a task] --> I[Unified interface receives the task]
    I --> P[Task decomposition and role assignment]
    P --> E[Different agents execute in parallel or sequence]
    E --> T[Agents hand off results to each other]
    T --> R[Unified interface consolidates progress]
    R --> J[Human review and decision]

The key is whether work can actually flow between agents.

One agent can handle understanding the problem; another handles execution; a third looks for errors; a fourth offers an independent perspective. A user or designated integrating agent then converges the results.

In this kind of setup, AI is no longer just a question-answering tool. Each agent becomes a node in a working network.

I wrote previously about this in Three Claudes, One Loop: How I Connected Design Exploration to Live Deployment, which described how different Claude interfaces can chain together via mocks, specs, and screenshots. The cross-agent workflow here goes a step further: the handoff partners are no longer just different interfaces within one product, but potentially agents running in different systems, different models, different permission environments.

Model Selection Follows the Task, Not the Brand

I update my Model Frontier Live Dashboard twice a week. It is not a fixed ranking. It is a selection map, organized by task type (writing, research, software development, everyday use), listing currently viable models and their costs.

This matters for cross-agent workflows. Models update, prices shift, and the same model can perform quite differently across task types. A model that fits a given role today may have a better alternative after the next release cycle.

So it would be misleading to write that Claude is always the planner, Codex always the executor, or that Antigravity and Grok each have a fixed personality. A more accurate framing: look at the task first, consult the current model data, then decide which agent takes which role in this particular round of work.

The reading order on the model page can be mapped directly onto a cross-agent selection process: first, check whether capability differences exceed the margin of error; if scores are close, compare costs; then confirm whether the benchmark actually matches your task; finally, factor in data sovereignty and security risk.

Figure 5 | Selecting agents for the current round based on task scenario

flowchart TB
    A[Input task scenario] --> B[Consult model selection map]
    B --> C{What is the primary constraint?}
    C --> D[Capability fit to task]
    C --> E[Cost and usage frequency]
    C --> F[Generalization on unfamiliar problems]
    C --> G[Safety and prompt injection risk]
    D --> H[Select agents for this round]
    E --> H
    F --> H
    G --> H
    H --> I[Set roles and permissions]
    I --> J[Execute, hand off, and review]

The map does not make the final call for you. Its purpose is to narrow the field so you can test candidates against your actual task. One thing to watch: results from long-horizon agentic coding runs should not be read as an overall ranking for everyday conversation, writing, or research. Cost figures also need a consistent basis. API usage costs and chat subscription fees are not the same thing.

This means agent roles are a working configuration, not a brand identity.

Different Agents, Different Roles in the Same Job

Cross-agent collaboration only works if you stop treating every agent as the same kind of tool.

Within a single task, agents can take on distinct roles:

The same agent can occupy different roles across different tasks.

One possible flow for a given task:

Figure 6 | Role division across agents within a single task

flowchart LR
    H[User defines task] --> C[Claude: understand and plan]
    C --> X[Codex: execute or modify]
    X --> G[Grok: offer independent perspective]
    G --> A[Antigravity: check or extend]
    A --> C2[Claude: integrate results]
    C2 --> H2[User: confirm whether to proceed]

This diagram shows roles, not a fixed pipeline. The actual flow must be shaped by the task at hand; there is no single path that applies everywhere.

The real question to keep asking is: which agent is best suited for this particular step?

Once work is broken into stages, an agent’s value is no longer determined only by the quality of a single response. It also depends on whether the agent can receive a task cleanly, deliver a result that can be verified, and leave the next agent with what it needs to continue.

Claude and Codex: Operating in Both Directions

What is already visible is a relationship closer to genuine collaboration. Claude can provide analysis and hand work off to Codex; Codex can execute independently and return results to Claude.

The forward handoff looks like this:

Figure 7 | Forward handoff flow between Claude and Codex

sequenceDiagram
    participant U as User
    participant C as Claude
    participant X as Codex
    participant I as Unified Interface

    U->>I: State the work objective
    I->>C: Route to Claude for analysis
    C->>I: Report task decomposition
    C->>X: Hand off concrete work
    X->>I: Report execution results and evidence
    I->>C: Provide results for integration
    C->>U: Propose next steps or items needing confirmation

The reverse flow:

Figure 8 | Reverse feedback from execution back to analysis

flowchart LR
    X[Codex executing] --> Q[Discovers a problem requiring re-analysis]
    Q --> C[Routes to Claude for reframing]
    C --> D[Claude proposes a judgment or alternative]
    D --> X2[Codex resumes execution]
    X2 --> U[User reviews]

What matters here is whether the task can flow.

A task does not have to terminate in any one tool. It can move from planning into execution, back into analysis, and eventually land as a result a human can review. Nothing about the chain requires it to stay inside a single interface. The structure is what gives it coherence.

This also builds on an earlier experiment I wrote about in I Let Codex Challenge Claude’s Work. But Didn’t Just Take Its Word For It, which was about preserving the independence of a second opinion. This article takes things further: when the second agent is no longer just reviewing but is being brought into the same workflow, the handoff and permission structure need deliberate design.

Having Claude Code Use Codex CLI for Image Generation is already a concrete cross-agent instance. One agent in a working environment borrowing another agent’s capability to complete a task that does not live within a single tool. The scope of this article is broader: from borrowing a single capability to coordinating multiple agents within one interface.

The Interface as a Work Control Surface

A unified interface is not just a place to collect different brands’ buttons. Its real role is giving the user a clear view of the entire job’s state.

At minimum, a good control surface should let you see:

Figure 9 | Work state that a unified interface needs to display

flowchart TB
    T[Current task] --> R[Currently responsible agent]
    R --> P[Current progress]
    P --> D[Completed work]
    D --> N[Next agent to take over]
    N --> H[Items requiring human confirmation]

Understood this way, the unified interface is a work control surface:

Figure 10 | Unified interface as a cross-agent work control surface

flowchart TB
    U[User]
    I[Unified Work Control Surface]
    T[Task planning]
    S[Agent selection]
    P[Permission management]
    V[Progress tracking]
    R[Result integration]
    H[Human review]
    U --> I
    I --> T
    I --> S
    I --> P
    I --> V
    I --> R
    I --> H

This surface holds the capabilities of different agents within the same working context.

The user starts by stating what they need to accomplish, then decides which agent is suited for the job, without first asking “which tool should I open?” That is a meaningful difference.

Tool-oriented work means people conform to tools. Task-oriented work means tools conform to the task.

A Complete Cross-Agent Task Flow

A reasonably complete cross-agent workflow breaks into nine steps:

Figure 11 | Nine-step flow for a cross-agent task

flowchart TB
    A[1. Input requirements] --> B[2. Decompose the task]
    B --> C[3. Select appropriate agents]
    C --> D[4. Set context and permissions]
    D --> E[5. Agents execute]
    E --> F[6. Hand off outputs and evidence]
    F --> G[7. Another agent reviews or extends]
    G --> H[8. Unified interface consolidates]
    H --> I[9. Human reviews, continues, or stops]

Each step carries its own responsibility.

“Input requirements” is about defining the objective. “Decompose the task” is about sequencing the work. “Select agents” is about role assignment. “Set context and permissions” is about establishing boundaries. “Hand off outputs and evidence” is about traceability. “Human review” is about final accountability.

Place multiple agents together without these structures and you mostly end up with several answers sitting side by side. Work only flows when tasks, data, and responsibility can be transferred along with it.

Handoffs Cannot Run on Copied Chat Logs

The most common failure in cross-agent work is dumping an entire chat history into the next agent.

This feels like preserving full context, but it often does not help. The receiving agent still has to figure out on its own:

• Which line is the actual task
• Which lines are background
• Which lines were just exploratory thinking
• What has been confirmed
• What is still a working assumption
• What must not be acted on

A handoff should be condensed into a simple working contract:

Task objective:
Current progress:
Known context:
Available data:
Data that cannot be used or shared externally:
Permitted operations:
Prohibited operations:
Expected output:
Acceptance criteria:
Stopping conditions:
Next agent:

The value of this document is that the receiving agent does not have to reconstruct the work’s boundaries from scratch.

A more complete handoff flow:

Figure 12 | Information flow from Agent A to Agent B

flowchart LR
    A[Agent A]
    B[Summarize task objective]
    C[Summarize known context]
    D[Flag evidence and items pending confirmation]
    E[Flag permissions and restrictions]
    F[Pass to Agent B]
    G[Agent B produces results with reasoning]
    H[Return to unified interface]
    I[Human or another agent reviews]
    A --> B --> C --> D --> E --> F --> G --> H --> I

A good handoff transmits what the next step actually needs, not the entire conversation history.

The More Convenient the Interface, the More Important the Permission Boundaries

A single interface for multiple agents dramatically lowers the barrier to use. That convenience also introduces new problems.

If an agent can operate other agents, who decides what it is allowed to do? If one agent can invoke another, does that authorization propagate indefinitely? If multiple agents can all read the same data, which of it can be shared freely and which must stay within its original scope?

Cross-agent workflows need to answer not only “can this be done?” but also “how far is it permitted to go?”

Permissions can be organized into tiers:

Figure 13 | Permissions moving from information access toward external action

flowchart TB
    A[View information] --> B[Analyze information]
    B --> C[Propose recommendations]
    C --> D[Modify working content]
    D --> E[Execute external operations]
    E --> F[Act on behalf of the user externally]
    F --> H[Human confirmation required]

The closer an action is to the outside world, the more it needs human confirmation.

The following should never happen automatically just because a workflow is running smoothly:

• Logging into accounts
• Using private data
• Reading or transmitting confidential material
• Using API keys
• Sending external messages
• Processing payments
• Publishing content
• Deploying websites
• Irreversible deletions or modifications

A unified interface can surface these actions in one place, but centralization is not the same as authorization.

The interface should make it clearly visible: which agent is currently active, what permissions it is exercising, and whether the next action requires confirmation from you.

More Agents Does Not Mean More Reliable Judgment

Multiple agents can help surface different angles, but their number alone does not produce correct answers.

They may share the same flawed premise, or arrive at similar wrong conclusions precisely because they received identical context.

Cross-agent collaboration therefore requires keeping three things distinct:

Figure 14 | Separating source data, agent analysis, and human judgment

flowchart TB
    A[Source data] --> B[Agent analysis]
    B --> C[User or other agent judgment]
    A -. Source must be preserved .-> C

These three layers should not be blended together.

During handoffs, it helps to mark clearly:

• What has been confirmed
• What comes from source data
• What is the agent’s inference
• What remains unverified
• What is only a provisional direction

This is why I do not treat “multiple agents agree” as proof.

Consensus can increase confidence. It cannot replace evidence.

Workflows Also Need Failure Paths

A workflow with only a success path is not complete.

Cross-agent collaboration can break at different points: an agent becomes unavailable, permissions are insufficient, context is incomplete, output formats do not match, an external service does not respond, or two agents reach contradictory conclusions on the same question.

Fallback paths belong in the design from the start:

Figure 15 | Correction, rerouting, and human takeover when an agent fails

flowchart TB
    A[Agent executes] --> B{Does the result meet acceptance criteria?}
    B -->|Yes| C[Pass to next step]
    B -->|No| D{Is the problem correctable?}
    D -->|Yes| E[Return to original agent for correction]
    D -->|No| F[Route to another agent for review]
    F --> G{Is human judgment needed?}
    G -->|No| C
    G -->|Yes| H[Pause and return to user]

There is an important implication here. When an agent fails, the system should surface that failure clearly. It should not quietly shift the task objective or lower the acceptance criteria to make the workflow look like it succeeded.

For the user, “your judgment is needed here” is itself a valid and useful outcome.

From Using Multiple Models to Building a Cross-Agent Work System

Claude, Codex, Antigravity, and Grok make it possible to assign different agents to different tasks.

What matters more is what kind of system those tools are placed inside.

A mature cross-agent work system should have:

Figure 16 | Core components of a mature cross-agent work system

flowchart TB
    A[Unified interface] --> B[Clear task decomposition]
    B --> C[Explicit role assignment]
    C --> D[Traceable agent handoffs]
    D --> E[Controlled permission boundaries]
    E --> F[Reviewable outputs]
    F --> G[Preserved human decision-making]

The center of this system is still the person.

You do not have to execute every step yourself, but you need to know what happened at each step. At the moments that count, the ability to stop and correct must remain yours.

For me, the most valuable change is this: I can now bring different agents into the same piece of work through a single interface, and let a task flow from planning into execution into review and integration.

People Are Starting to Design Their Own Agent Working Environments

The old question was:

Which AI is best for me?

The better question now is:

Can I set up an interface where different agents each handle the work they are suited for?

The answer depends on how model capability, interface design, task handoffs, permission management, and human judgment are assembled together.

When Claude can operate Codex, Codex can return to Claude, and the work can extend to Antigravity and Grok, using AI is no longer just “open a chat window and wait for an answer.”

Those of us already building this way are constructing working environments where humans set the direction and multiple agents collaborate to carry it out.

One interface solves the tool-switching problem. Cross-agent handoffs solve the workflow continuity problem.

Permissions and human review determine whether the system is worth trusting. What matters is whether we can get multiple agents to hand off to each other (within clear boundaries) on the same piece of work. Every worker in the post-AI era will need to design and refine the system that fits their own way of working.