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A quiet change to OpenAI Codex in June 2026 removed something builders have come to rely on: the ability to read what one agent tells another.

PR #26210, merged on June 5, 2026, encrypted the message payloads exchanged between parent agents and child agents in Codex’s MultiAgentV2 orchestration layer. Starting with version 0.137.0, the three tools that wire multi-agent pipelines together — spawn_agent, send_message, and followup_task — no longer carry human-readable content. They carry ciphertext.

As The Register reported on July 15: “Responses encrypts the message argument returned by the model, Codex forwards only that ciphertext, and Responses decrypts it internally for the recipient model.” OpenAI provided no rationale for the change.

What Changed, Technically

Before the change, inter-agent instructions in MultiAgentV2 passed through two parallel fields in InterAgentCommunication:

  • content — human-readable plaintext, stored locally in rollout history and visible in trace logs
  • encrypted_content — not present or empty

After PR #26210, the fields are reversed. The content field is empty. Only encrypted_content is populated — and only the Responses API backend can decrypt it. The receiving sub-agent gets the decrypted payload; the developer’s local tooling gets nothing.

The three tools affected are:

Tool Purpose
spawn_agent Parent agent creates a new sub-agent and assigns it a task
send_message Parent sends a follow-up instruction to a running sub-agent
followup_task Agent chain passes a continuation task to the next agent

This affects all multi-agent workloads using Sol or Terra models post-v0.137.0. If you upgraded Codex after June 5, the change is already live in your environment.

What Builders Lose

The practical impact is significant for anyone running multi-agent workflows:

Audit trails go dark. Before, you could open a rollout history file and answer “what task did this spawn_agent call give to the sub-agent?” Now that question has no local answer. The task text exists only in decrypted form inside OpenAI’s infrastructure.

Debugging becomes guesswork. When a multi-agent pipeline fails — wrong result, stalled sub-agent, unexpected tool call — the first question is usually “what instruction did the parent send?” That question is now unanswerable from the developer side without external logging.

Compliance surfaces a gap. Builders subject to audit requirements (SOC 2, HIPAA, internal security reviews) that require logging what AI agents instruct each other to do now have a structural gap. The instructions exist; they’re just inaccessible.

As one developer noted in GitHub issue #28058, which was filed specifically to track this regression: “We don’t want to build Skynet and then be unable to audit what it’s doing.”

What the Community Is Proposing

The GitHub issue proposes a practical fix that OpenAI has not yet implemented: add a plaintext companion field to the encrypted payload in rollout metadata.

The proposed structure keeps ciphertext for delivery but populates InterAgentCommunication.content with a readable audit string that is stored in local rollout metadata — not sent to the sub-agent. A prototype implementation was shared for spawn_agent.

As of July 15, OpenAI has not responded to the issue.

Builder Mitigation Strategies

Until OpenAI either reverts the change or implements the companion-field approach, there are several ways to maintain auditability in multi-agent Codex workflows:

Log at the call site. The parent agent constructs the task string before calling spawn_agent. That string is still in your application’s scope — log it before passing it to the Codex API. This requires instrumenting your orchestration code rather than relying on Codex’s built-in trace surface, but it restores visibility at the point where you still have access.

Use the Responses API trace. The Responses API returns structured output including tool calls and their arguments. If you capture the full Response object when a spawn_agent call is made, the parent model’s tool call arguments — which include the task description — are returned as structured JSON before encryption occurs in the inter-agent transport layer. This gives you a partial audit trail at the API layer even when the local Codex tooling is blind.

Build a proxy agent layer. If you need strict auditability across a long multi-agent chain, consider inserting a thin logging proxy between each parent-child hop: every spawn_agent call passes through your layer, which logs the plaintext task and then forwards the call. This is architecture overhead, but it gives you a durable audit log independent of Codex’s internal decisions.

Evaluate alternative frameworks for audit-critical workflows. For workflows where auditability is non-negotiable — healthcare data processing, financial document analysis, internal security tooling — frameworks like LangGraph or Anthropic’s multi-agent patterns maintain plaintext task descriptions throughout the agent lifecycle, because the orchestration runs in your process rather than inside an opaque hosted runtime.

OpenAI’s Likely Rationale (Unofficial)

OpenAI has not explained the change. Developer discussion in the GitHub thread points to two plausible reasons:

Privacy hardening. If task instructions contain sensitive data from user documents or memory, encrypting them prevents exposure through local logs or debug surfaces. This is a reasonable privacy posture for an enterprise product, though it should not have come at the cost of auditability.

Competitive obfuscation. Some developers speculate the encryption prevents analysis of how Codex’s multi-agent orchestration works internally — particularly how it decomposes complex tasks into sub-agent assignments. Whether or not that is the intent, it is an effect.

Neither explanation was offered officially. Until OpenAI publishes a rationale, the change reads as a unilateral tradeoff where developer observability lost.

What to Watch

OpenAI has not responded to issue #28058. The most likely resolutions are:

  1. OpenAI adds a plaintext companion field in rollout metadata (the community’s proposed fix)
  2. OpenAI documents the rationale and provides an explicit opt-in mechanism for developers who need audit trails
  3. The issue remains open indefinitely while the encrypted behavior stays as-is

Builders who cannot accept opaque agent orchestration should log at the call site or evaluate alternative frameworks now rather than waiting for a resolution that may not come.

Primary sources: The Register coverage (July 15, 2026); GitHub issue #28058; Developers Digest coverage; Codex changelog.