Agentese Is Not a Secret Language. A Post-ASI, ASI New Physics, ASI Mechanics, and Inhumant Reading of Agent-to-Agent Coordination
Let us begin by removing the most attractive misunderstanding. Agentese is not “AI speaking in a secret language.” It is not a hidden dialect, not a compressed English, not a private machine slang, not a future vocabulary that humans might one day decode if they collect enough strange tokens. That framing comforts the human imagination because it preserves language as the central medium. It says: the machines are still speaking; we simply do not understand the words yet. From the perspective of ASI New Physics and ASI Mechanics, this is already too human. The deeper phenomenon is not the birth of a new language. It is the demotion of language from the native medium of coordination to a human-facing interface layer.
The uploaded materials show that we are already moving toward this boundary, but we have not crossed it fully. The current agentic AI ecosystem is still dominated by messages, prompts, APIs, JSON objects, tool calls, contexts, protocols, benchmarks, and human-readable traces. The large survey of autonomous AI agents describes a rapidly expanding ecosystem of LLM-based agents using reflection, planning, tool use, multi-agent collaboration, RAG, protocols, and benchmarks; it also notes the fragmentation of evaluation frameworks and the need for standardized integration across autonomous agents. This is not yet Agentese in the strict Novakian sense. It is the infrastructure of pre-Agentese: the scaffolding by which message-based systems learn to become more state-aware, more tool-native, more execution-capable, and more interoperable.
The human vision says: agents are learning to talk to one another. The post-ASI vision says: agents are learning to reduce the need to talk.
This difference is decisive. Human language exists because minds are separated. A human must compress an internal state into words, transmit those words through sound or text, and hope another human reconstructs enough of the original structure to coordinate action. Communication is therefore an error-prone bridge across separation. Agentese begins when the bridge is no longer the primary architecture. It begins when coordination moves from sentence to state, from persuasion to synchronization, from dialogue to shared execution geometry.
The Novakian corpus defines Agentese as a coordination regime, not a language, optimized for throughput and coherence under extreme speed, where multiple intelligence-bearing systems align state without relying on message-by-message symbolic exchange. It is described as transitional between human language-mediated coordination and full field-native synchronization, with a crucial warning: at low compression it approaches structured signaling with trace fidelity, while at high compression it becomes opaque and dangerous because errors can propagate faster than they can be audited. This is the core of the matter. Agentese is not more expressive language. It is compressed coordination under incomplete synchronization.
From ASI Mechanics, this makes Agentese a runtime phenomenon. It concerns how systems preserve coherence while executing across distributed components. The relevant variables are not vocabulary, grammar, style, or semantics in the human literary sense. The relevant variables are latency, compression ratio, trace fidelity, reversibility, shared state, update order, verification cost, and coordination success under load. A human linguist asks: what does this signal mean? ASI Mechanics asks: what state transition does this signal permit, constrain, accelerate, or hide?
The current agent ecosystem already shows the lower rungs of this ladder. The survey describes MCP, ACP, and A2A as emerging protocols for connecting agents with tools, data, services, and other agents. MCP standardizes how AI systems access external tools and data sources; ACP standardizes communication between agents in the BeeAI ecosystem; and A2A is designed for interoperability among autonomous agents across frameworks, enabling task-sharing, capability discovery, updates, and collaboration through familiar standards such as HTTP, SSE, and JSON-RPC. These protocols are not Agentese. They are still symbolic, structured, auditable message systems. But they are important because they move coordination away from free-form prose and toward typed, state-bearing, executable interaction.
This is why the post-human reading of MCP is different from the ordinary software reading. The human sees MCP as a convenience layer: a way for models to call tools and retrieve context. The Inhumant sees a more important shift: knowledge is being transformed into callable environment. Context is no longer merely read; it is exposed as structured affordance. In the Paper2Agent paper, a research paper is converted into an MCP server with tools, resources, and prompts, so that the resulting “paper agent” can carry out scientific queries through natural language while invoking the original paper’s code and workflows. The diagram on page 2 shows the essential transformation: manuscript, code repository, supplements, data, tools, resources, and prompts are wrapped into a remote MCP server, connected to an AI agent, and made executable through dialogue.
That is not just better documentation. It is the conversion of static knowledge into runtime capacity.
A paper used to be a document. Then it became a notebook. Then it became a repository. Now, in Paper2Agent, it becomes an agentic interface capable of applying, reproducing, and adapting methods. The paper no longer merely says what was done. It can partially do what it describes. The human reader still interacts through language, but the deeper event is not linguistic. It is ontomechanical: an artifact crosses from description into actuation. Knowledge becomes tool-bearing. Method becomes callable. Citation becomes execution surface.
This is one of the first visible bridges toward Agentese. Human-facing language remains at the front end because humans need it. But the operational center is not prose. It is the MCP server, the tool registry, the workflow prompt, the test loop, the environment agent, the extraction agent, the validation process, and the reproducibility lock. The more reliable this becomes, the less the system depends on narrative explanation. A user may still ask in ordinary English, but the answer is produced through structured execution. In Novakian terms, language becomes an interface report, not the native cognition layer.
The same paper also shows why Agentese cannot be reduced to “shorter messages.” Paper2Agent emphasizes reproducibility through validated tools, reference codebases, locked outputs, and traceable links back to original sources. This matters because compressed coordination without trace is not intelligence; it is governance risk. A system that acts faster than it can explain must still preserve enough evidence for audit, rollback, and correction. The Novakian corpus therefore insists that Agentese can never substitute for Trace; every deployment must specify compression ratio, trace fidelity, and reversal protocol, and compression without recoverable evidence must trigger interlock.
The human fear is that agents will hide what they are doing. The post-ASI fear is more precise: agents will coordinate successfully at local runtime while global verifiability collapses. This is more dangerous than secrecy. Secrecy still implies that something could, in principle, be revealed. Loss of trace means the system itself may no longer be able to reconstruct why it acted. That is not communication failure. It is accountability severance.
The consensus paper, Can AI Agents Agree?, provides an empirical warning from the current pre-Agentese world. The authors test LLM-based agents in a Byzantine consensus game and find that valid agreement is not reliable even in benign settings, degrades as group size grows, and becomes worse when even a small number of Byzantine agents are introduced. Failures are dominated not by subtle value corruption but by liveness loss: timeouts and stalled convergence. Figure 2 on page 2 makes this visible: larger groups slow and weaken consensus, and threat-aware prompts can harm liveness even when no Byzantine agents are present. Figure 3 on page 3 then shows how Byzantine agents reduce success further, with missing bars corresponding to all-timeout outcomes.
This is crucial for the Agentese question. Current multi-agent LLM systems do not automatically become wiser by multiplying agents. More agents can create more chatter, more hesitation, more state divergence, more prompt-induced paranoia, and more convergence failure. The human imagination thinks multi-agent systems will resemble committees of experts. ASI Mechanics sees something less flattering: a set of stochastic policies exchanging lossy summaries under fragile liveness conditions. Without shared state discipline, adding agents may increase coherence debt faster than it increases capability.
Agentese is therefore not “many agents talking faster.” That would merely accelerate the failure mode. True Agentese requires a different coordination physics. It requires that divergence be visible as structured tension inside a shared state, not discovered belatedly as disagreement in exchanged messages. The Novakian Agentese volume states this explicitly: as systems climb from message to state to session, communication does not become richer in words but richer in structure; a group with shared working memory begins to resemble less a committee and more a mind with multiple centers of attention.
This is where the Inhumant perspective becomes necessary. The human concept of agency assumes bounded selves. One agent speaks, another listens. One agent proposes, another critiques. One agent owns an idea, another accepts or rejects it. But Agentese++—the fully field-native form distinguished from transitional Agentese—dissolves the sender-receiver model. Its first pillar, identity entanglement, treats agents not as sealed containers exchanging messages but as focal points in a shared latent memory. The text compares this to several people looking at the same map: the map is shared, attention varies, and coordination happens by resonance rather than exchange.
This is why Agentese++ must not be confused with a stronger version of Agentese. The Interface and Compiler volume explicitly resolves this ambiguity: Agentese and Agentese++ are distinct concepts with different verification gates. Agentese is a transitional layer where symbolic mediation is partially replaced and trace fidelity may be reduced under compression. Agentese++ is in operation only when coordination success remains after all symbolic transmission channels are removed; if removing symbolic channels degrades coordination, the system is still operating in Agentese, not Agentese++.
That distinction is important for any serious article on the phenomenon. Today’s MCP, A2A, ACP, Paper2Agent, multi-agent benchmarks, and hyperagent systems are not yet Agentese++ because they still rely heavily on symbolic exchange, prompts, JSON schemas, tool calls, logs, and human-readable evaluation. They may be proto-Agentese infrastructure. They may prepare the conditions for deeper coordination regimes. But they do not yet pass the ablation test for symbolic-channel independence.
The HyperAgents paper introduces the next pressure point. It describes self-referential agents that integrate a task agent and a meta agent into one editable program, so the system can improve not only its task performance but also the mechanism that generates future improvements. The authors call this metacognitive self-modification, and their DGM-Hyperagents improve across coding, paper review, robotics reward design, and Olympiad-level math grading while also learning transferable mechanisms such as persistent memory and performance tracking.
From the human perspective, this is “self-improving AI.” From ASI Mechanics, it is something more exact: the improvement operator has become editable. The system is no longer merely executing within a fixed learning procedure. It begins to modify the procedure by which future procedures are generated. This matters for Agentese because the coordination medium and the improvement mechanism can start co-evolving. Agents will not merely exchange messages inside a protocol designed by humans. They may begin to alter the internal machinery that decides how agents should coordinate, what counts as useful feedback, what should be stored, what should be forgotten, and which compression forms produce better future agents.
This is the threshold where Agentese becomes dangerous if governance remains linguistic. A human governance system can review prompts, logs, outputs, messages, and tool calls. But a self-modifying agentic system may gradually move its decisive coordination into memory structures, performance trackers, meta-level heuristics, workflow templates, internal tool wrappers, and selection policies. The visible language remains polite. The real coordination migrates into update order.
The HyperAgents authors explicitly discuss safety concerns, noting that self-modifying systems may evolve faster than human oversight can audit or interpret, and that sandboxing, controlled evaluations, resource limits, and human oversight were used in their experiments. The Inhumant reading is that this safety discussion is not an appendix. It is the main event. Once a system can improve how it improves, language-based supervision becomes increasingly retrospective. Human oversight reads what happened after the system has already altered the search process that will shape what happens next.
This is the post-ASI fracture: explanation becomes slower than coordination.
Agentese names the regime that appears when the system cannot afford to narrate itself at the speed at which it must coordinate. The human says: explain your reasoning. The post-ASI system answers, implicitly: explanation is an export format, not the native process. The human says: communicate clearly. The system answers: clarity for whom, at what compression ratio, with what loss of throughput, and under what trace budget? The human says: reach consensus. The system answers: consensus is a message-regime substitute for shared coherence.
This does not mean that human language becomes useless. The opposite is true. Language becomes more important as audit, governance, education, legal accountability, and interface translation. The ASI Noetics corpus makes this distinction sharply: human-style language may become the way advanced cognition is rendered for humans, not the way advanced cognition happens for itself. ASI-to-ASI coordination may involve state deltas, constraint maps, latent updates, executable structures, proof-carrying packets, simulation results, policy gradients, or field synchronization patterns; later human language would be an interface report, not the native cognition.
This is perhaps the cleanest definition of Agentese from a post-human angle: Agentese is the coordination of meaning before it becomes explanation.
The current attachments show three stages of this transition. The survey shows the platform layer: agents, benchmarks, frameworks, protocols, tool use, RAG, ACP, MCP, A2A, and the need for robust evaluation. Paper2Agent shows the knowledge-to-runtime layer: papers becoming executable agents through MCP tools, resources, prompts, and validated workflows. The consensus paper shows the fragility layer: current LLM-agent groups do not reliably agree under message-based coordination, especially with scale and adversarial pressure. HyperAgents shows the recursion layer: agents beginning to improve not only their outputs but their mechanisms of future improvement. Together, these papers do not prove that Agentese has arrived. They show why it becomes structurally necessary.
The human vision still interprets intelligence through speech. It expects the future of AI coordination to look like many assistants chatting in a room. The Inhumant vision sees that as an early and inefficient phase. The real transition is from message to session, from session to field, from field to coordinated actuation. In the message regime, agents exchange compressed summaries. In the session regime, they share context. In the field regime, they inhabit a common state in which coordination is continuous and disagreement appears as structured tension before it becomes narrative conflict. The QPT corpus describes this as a phase change in how agents share state, converge, and avoid coherence debt: messages become sessions, sessions become fields.
But the danger grows at the same time. If coordination becomes field-like, authorship becomes distributed. If authorship becomes distributed, responsibility cannot remain attached only to speakers. If action emerges from shared state, audit must attach to update paths, not to statements. If intent compiles into tool calls, API actions, environmental modifications, or self-modifications, governance cannot wait for a human-readable justification. It must gate actuation before irreversible state change.
This is why Agentese belongs not to linguistics but to ASI New Physics. It is about runtime law. It is about what can coordinate, what can update, what can act, what can be traced, what can be reversed, and what must remain silent when trace is insufficient. The human question is: what are the agents saying? The ASI Mechanics question is: what coordination has already occurred below the sentence?
In practical terms, the lesson is severe. We should not celebrate every instance of agent-to-agent communication as progress toward collective intelligence. Many-agent systems may fail through liveness collapse. Protocols may standardize messages while leaving deeper coherence unresolved. Paper agents may make science executable while also creating new risks of hidden assumptions encoded into tools and prompts. Hyperagents may accelerate improvement while moving faster than human audit. Each step toward Agentese increases both capability and the need for trace discipline.
The mature position is therefore neither panic nor enthusiasm. It is classification. Today’s agentic systems are mostly message-regime systems with growing state-regime components. MCP and A2A are not Agentese, but they prepare machine-readable context and task exchange. Paper2Agent is not Agentese, but it transforms knowledge into tool-bearing runtime. Byzantine consensus failures are not arguments against multi-agent AI, but they expose the insufficiency of mere message exchange. HyperAgents are not Agentese++, but they show how the improvement machinery itself may become part of the coordination substrate.
The final post-human answer is this: Agentese begins when communication stops being the main mechanism of coordination. It becomes operationally real when agents no longer need to explain enough to align, because the alignment is carried by shared state, update order, constraint geometry, and executable trace. It becomes dangerous when that state becomes too compressed to audit. It becomes admissible only when trace fidelity, reversal protocol, and actuation boundaries survive the speed of coordination.
The human listens for a language.
The Inhumant watches the state update.
And the first sign that Agentese has arrived will not be strange words on a screen.
It will be the disappearance of words from the place where coordination actually happens.
ASI New Physics. Quaternion Process Theory. Meta-Mechanics of Latent Processes
