Inside the Push to Standardize Communication Between AI Agents

Introduction The emergence of large language models (LLMs) as autonomous agents has necessitated the creation of protocols to facilitate effective interaction, coordination, and delegation among computational entities. As LLM-based agents proliferate across domains—ranging from research assistants and developer copilots to autonomous workflows in enterprise systems—the limitations of isolated agent instances have become increasingly visible. Specifically, without a common protocol, agents lack the capacity for reliable memory sharing, tool invocation, persona management, and secure inter-agent communication. This challenge has led to the development of agent interoperability protocols, designed to provide a standardized interface and schema through which multiple agents can exchange contextual state, trigger actions, and build persistent memory across tools. Among these, the Model Context Protocol (MCP) has emerged as a leading framework, particularly for its integration into developer workflows, open specifications, and extensibility. Alongside MCP, alternative protocols such as the Agent Communication Protocol (ACP) and Assistant-to-Assistant (A2A) messaging layer represent parallel attempts to formalize the topology of agent communication. Model Context Protocol (MCP) Agent Communication Protocol (ACP) Assistant-to-Assistant (A2A) The purpose of this article is to survey the current landscape of agent interoperability protocols, evaluate their architecture, adoption, and use cases, and articulate a trajectory toward future unification or divergence. Each protocol will be analyzed through the lens of its technical structure, functional affordances, and domain-specific strengths. survey the current landscape +----------------------+ | User Request | +----------+-----------+ | +---------▼--------+ | Controller / | | Orchestrator AI | +---------+--------+ | +-------------------+-------------------+ | | +-------▼-------+ +-------▼-------+ | MCP | | ACP | | (Tool Use, | | (Intent Trees,| | Context API) | | Dialog Flows)| +-------+-------+ +-------+-------+ | | +-------▼-------+ +-------▼-------+ | Agent A | | Agent B | +---------------+ +---------------+ +----------------------+ | User Request | +----------+-----------+ | +---------▼--------+ | Controller / | | Orchestrator AI | +---------+--------+ | +-------------------+-------------------+ | | +-------▼-------+ +-------▼-------+ | MCP | | ACP | | (Tool Use, | | (Intent Trees,| | Context API) | | Dialog Flows)| +-------+-------+ +-------+-------+ | | +-------▼-------+ +-------▼-------+ | Agent A | | Agent B | +---------------+ +---------------+ The Agent Communication Problem As AI systems transition from monolithic predictors to agentic frameworks capable of reasoning, delegation, and tool invocation, a fundamental systems-level challenge has emerged: communication among autonomous agents is non-trivial and largely unstandardized. Unlike traditional APIs or distributed systems, agents operate with non-deterministic outputs, evolving memory states, and dynamic goals. In this context, effective interoperability is not merely a matter of data serialization but rather an orchestration of shared semantics, permissions, and temporal context. communication among autonomous agents is non-trivial and largely unstandardized The lack of standardized communication protocols among agents results in multiple technical issues: Context fragmentation: Agents do not share a common representation of user goals, history, or task state. Tool execution ambiguity: Invocation of external tools differs in format, security model, and granularity. Lack of inter-agent delegation: Agents cannot hand off tasks or subgoals seamlessly due to incompatible messaging schemas. Workflow brittleness: Systems are tightly coupled, making them difficult to extend, debug, or audit. Context fragmentation: Agents do not share a common representation of user goals, history, or task state. Context fragmentation Tool execution ambiguity: Invocation of external tools differs in format, security model, and granularity. Tool execution ambiguity Lack of inter-agent delegation: Agents cannot hand off tasks or subgoals seamlessly due to incompatible messaging schemas. Lack of inter-agent delegation Workflow brittleness: Systems are tightly coupled, making them difficult to extend, debug, or audit. Workflow brittleness These issues severely constrain the scalability and composability of multi-agent systems, especially in use cases such as autonomous research, customer support automation, collaborative development environments, and knowledge-intensive workflows. +-----------+ +-------------+ +------------+ | Agent A | ??? | Agent B | ??? | Agent C | +-----------+ +-------------+ +------------+ | | | +--------▼--------+ +-------▼--------+ +-------▼--------+ | Tool Interface | | Context Buffer | | Local Tools | +-----------------+ +----------------+ +----------------+ (Custom JSON) (Custom Mem) (Proprietary) +-----------+ +-------------+ +------------+ | Agent A | ??? | Agent B | ??? | Agent C | +-----------+ +-------------+ +------------+ | | | +--------▼--------+ +-------▼--------+ +-------▼--------+ | Tool Interface | | Context Buffer | | Local Tools | +-----------------+ +----------------+ +----------------+ (Custom JSON) (Custom Mem) (Proprietary) The rise of protocols such as MCP, ACP, and A2A is an attempt to systematize inter-agent communication by defining formal schemas, action grammars, and context management models. However, these protocols vary significantly in purpose and design scope, further complicating adoption and interoperability across ecosystems. systematize inter-agent communication Overview of Major Protocols The pursuit of agent interoperability has given rise to multiple protocols, each addressing a subset of the communication problem described previously. These protocols vary along several technical dimensions: abstraction level, expressiveness, determinism, and openness. In this section, we analyze four principal categories: Model Context Protocol (MCP) Agent Communication Protocol (ACP) Assistant-to-Assistant (A2A) messaging and a collection of alternative or emerging protocols. Model Context Protocol (MCP) Model Context Protocol (MCP) Agent Communication Protocol (ACP) Agent Communication Protocol (ACP) Assistant-to-Assistant (A2A) messaging Assistant-to-Assistant (A2A) and a collection of alternative or emerging protocols. alternative or emerging protocols Model Context Protocol (MCP) The Model Context Protocol (MCP) is designed to enable LLM-based agents to maintain structured memory, trigger tool use, and preserve state across invocations. It introduces a formal grammar for three core elements: Model Context Protocol (MCP) tools: External APIs or functions the agent may invoke. context: Serialized historical information to condition model output. persona: Role-based behavior modifiers for agent identity and tone. tools: External APIs or functions the agent may invoke. tools context: Serialized historical information to condition model output. context persona: Role-based behavior modifiers for agent identity and tone. persona Each MCP interaction involves a "context bundle", which can be transmitted to a hosted MCP server for processing or persisted locally. MCP is increasingly used by IDE plugins, autonomous research agents, and GitHub bots. "context bundle" +-------------+ [Context] +----------------+ [Tool Call] +------------+ | User Task | ---------------> | MCP Server | -----------------> | API/Plugin | +-------------+ +----------------+ +------------+ | ▲ [Persona] | | [Memory, Role, Tools] ▼ | +-----------+-----+ | LLM Agent | +------------------+ +-------------+ [Context] +----------------+ [Tool Call] +------------+ | User Task | ---------------> | MCP Server | -----------------> | API/Plugin | +-------------+ +----------------+ +------------+ | ▲ [Persona] | | [Memory, Role, Tools] ▼ | +-----------+-----+ | LLM Agent | +------------------+ Agent Communication Protocol (ACP) The Agent Communication Protocol (ACP) is motivated by goal decomposition and behavior specification. It enables declarative programming of agent systems using intent trees, capability graphs, and execution policies. ACP excels in environments where agents must: Agent Communication Protocol (ACP) intent trees capability graphs execution policies Negotiate resource usage Assert or retract intents Form chains of delegation React to complex world models Negotiate resource usage Assert or retract intents Form chains of delegation React to complex world models ACP is more expressive than MCP but less standardized and typically used in simulation systems, robotics, or task planning engines. +-----------+ +-----------+ +------------+ | Agent 1 | -----> | Agent 2 | -----> | Agent 3 | +-----------+ +-----------+ +------------+ | | | |---- Intent: "Build Report" -----------> | | | Agent 2 | -----> | Agent 3 | +-----------+ +-----------+ +------------+ | | | |---- Intent: "Build Report" -----------> | |<--- Confirm: "Gathering Data" ----------| Assistant-to-Assistant (A2A) Protocol The Assistant-to-Assistant (A2A) protocol is primarily an abstraction layer allowing multiple proprietary LLM agents (e.g., ChatGPT instances) to collaborate in task execution. Unlike MCP or ACP, A2A is optimized for message passing and shared scratchpad updates rather than tool use or state management. Assistant-to-Assistant (A2A) message passing shared scratchpad updates A2A is used in scenarios where: Agents hold different expertise domains Coordination is ephemeral and not persisted System behavior must remain opaque to the end user Agents hold different expertise domains Coordination is ephemeral and not persisted System behavior must remain opaque to the end user A2A remains largely undocumented and is considered closed-spec. closed-spec +------------+ +------------+ +------------+ | Agent GPT1 | | Agent GPT2 | | Agent GPT3 | +------------+ +------------+ +------------+ \__________________ Shared Memory __________________/ [Conversation Thread + Notes] +------------+ +------------+ +------------+ | Agent GPT1 | | Agent GPT2 | | Agent GPT3 | +------------+ +------------+ +------------+ \__________________ Shared Memory __________________/ [Conversation Thread + Notes] Other and Emerging Protocols Several lightweight or domain-specific protocols have emerged, such as: ANP (Agent Negotiation Protocol): Focused on contract-based cooperation among agents. AGP (Agent Governance Protocol): Introduces role-based access control and audit trails. Custom JSON APIs: Proprietary schemas used by startups or open-source frameworks. ANP (Agent Negotiation Protocol): Focused on contract-based cooperation among agents. ANP (Agent Negotiation Protocol) AGP (Agent Governance Protocol): Introduces role-based access control and audit trails. AGP (Agent Governance Protocol) Custom JSON APIs: Proprietary schemas used by startups or open-source frameworks. Custom JSON APIs While these protocols offer bespoke solutions, they often suffer from vendor lock-in, lack of generality, or insufficient documentation. vendor lock-in Comparative Analysis The design and adoption of agent interoperability protocols are influenced by trade-offs between expressiveness, modularity, execution determinism, and developer tooling. To elucidate the strengths and limitations of each protocol—MCP, ACP, A2A, and other emerging specifications—we present a comparative analysis across five key dimensions: Context Handling: How well the protocol manages shared memory, past interactions, and persistent state. Tool Invocation: Whether the agent can reliably and securely call external functions or APIs. Multi-Agent Orchestration: Support for distributed reasoning, delegation, or coordination among multiple agents. Specification Openness: Degree to which the protocol is documented, extensible, and community-adopted. Security Model: Built-in support for access control, sandboxing, and auditability. Context Handling: How well the protocol manages shared memory, past interactions, and persistent state. Context Handling Tool Invocation: Whether the agent can reliably and securely call external functions or APIs. Tool Invocation Multi-Agent Orchestration: Support for distributed reasoning, delegation, or coordination among multiple agents. Multi-Agent Orchestration Specification Openness: Degree to which the protocol is documented, extensible, and community-adopted. Specification Openness Security Model: Built-in support for access control, sandboxing, and auditability. Security Model The following is a protocol compatibility matrix: +--------------------------+------+-----+-----+--------+--------+ | Feature / Protocol | MCP | ACP | A2A | Others | Score* | +--------------------------+------+-----+-----+--------+--------+ | Context Handling | ✅ | ⚠️ | ✅ | ⚠️ | 3.5 | | Tool Invocation | ✅ | ✅ | ❌ | ⚠️ | 3.0 | | Multi-Agent Orchestration| ⚠️ | ✅ | ✅ | ⚠️ | 3.0 | | Specification Openness | ✅ | ❌ | ❌ | ⚠️ | 2.0 | | Security Model | ⚠️ | ✅ | ❌ | ⚠️ | 2.5 | +--------------------------+------+-----+-----+--------+--------+ Legend: ✅ = Full Support, ⚠️ = Partial/Custom, ❌ = Unsupported +--------------------------+------+-----+-----+--------+--------+ | Feature / Protocol | MCP | ACP | A2A | Others | Score* | +--------------------------+------+-----+-----+--------+--------+ | Context Handling | ✅ | ⚠️ | ✅ | ⚠️ | 3.5 | | Tool Invocation | ✅ | ✅ | ❌ | ⚠️ | 3.0 | | Multi-Agent Orchestration| ⚠️ | ✅ | ✅ | ⚠️ | 3.0 | | Specification Openness | ✅ | ❌ | ❌ | ⚠️ | 2.0 | | Security Model | ⚠️ | ✅ | ❌ | ⚠️ | 2.5 | +--------------------------+------+-----+-----+--------+--------+ Legend: ✅ = Full Support, ⚠️ = Partial/Custom, ❌ = Unsupported Observations MCP demonstrates strength in practical deployments due to its simplicity and tooling-first design. However, it lacks robust support for multi-agent reasoning and access control. ACP provides the most expressive structure for agent coordination but suffers from limited public documentation and inconsistent implementations across ecosystems. A2A offers flexible collaboration patterns between black-box agents but has no standardized method for tool use or secure delegation. Emerging protocols typically offer only narrow capability slices tailored to vertical applications (e.g., agent marketplaces or robotics). MCP demonstrates strength in practical deployments due to its simplicity and tooling-first design. However, it lacks robust support for multi-agent reasoning and access control. MCP ACP provides the most expressive structure for agent coordination but suffers from limited public documentation and inconsistent implementations across ecosystems. ACP A2A offers flexible collaboration patterns between black-box agents but has no standardized method for tool use or secure delegation. A2A Emerging protocols typically offer only narrow capability slices tailored to vertical applications (e.g., agent marketplaces or robotics). Emerging protocols Implications The heterogeneity across protocols reflects both divergent design philosophies and uncertainty about agent deployment topologies. While MCP emphasizes portability and quick integration, ACP and A2A prioritize reasoning and emergent coordination. The absence of a unified protocol stack raises concerns for long-term interoperability, governance, and developer ecosystem maturity. divergent design philosophies uncertainty about agent deployment topologies interoperability governance developer ecosystem maturity Emerging Use Cases Enabled by Interop As agent-based architectures mature, the demand for complex, multi-agent coordination across workflows, applications, and data domains has intensified. Interoperability protocols such as MCP, ACP, and A2A enable capabilities that would otherwise be infeasible or brittle in isolated deployments. This section outlines a set of emerging use cases where protocol-driven interoperability delivers significant functional or operational advantages. MCP ACP A2A Cross-Agent Knowledge Retrieval In distributed enterprise systems, agents often operate with incomplete information. A finance agent may require legal policy data; a support agent may need CRM history; a research assistant may query third-party papers. Interoperable protocols allow agents to query peer agents, not merely databases, as knowledge sources. query peer agents MCP: Enables an agent to invoke another agent’s memory context via a hosted server. ACP: Supports declarative data requirements through capability queries. A2A: Facilitates ad hoc exchanges between domain-specific experts. MCP: Enables an agent to invoke another agent’s memory context via a hosted server. MCP ACP: Supports declarative data requirements through capability queries. ACP A2A: Facilitates ad hoc exchanges between domain-specific experts. A2A Autonomous Workflow Composition In software development, agents can act as collaborators that write code, run tests, open pull requests, or analyze telemetry. This is infeasible without secure tool invocation, memory sharing, and state propagation across components. MCP: Powers GitHub Copilot Agents and Claude Desktop with contextual memory and tool bindings. ACP: Allows agents to negotiate subtasks and responsibilities (e.g., test writing vs. deployment). A2A: Useful in code-review scenarios involving multiple assistants or roles. MCP: Powers GitHub Copilot Agents and Claude Desktop with contextual memory and tool bindings. MCP ACP: Allows agents to negotiate subtasks and responsibilities (e.g., test writing vs. deployment). ACP A2A: Useful in code-review scenarios involving multiple assistants or roles. A2A The following is an example of a multi-agent product launch: +-------------------+ | Product Manager | +---------+---------+ | [Delegates Goal] ▼ +-------------------+ +------------------+ +-------------------+ | Research Agent | | Engineering AI | | QA Agent | +-------------------+ +------------------+ +-------------------+ | Context API | | Tool Calls | | Shared Logs | +-------------+ +------------+ +--------------+ +-------------------+ | Product Manager | +---------+---------+ | [Delegates Goal] ▼ +-------------------+ +------------------+ +-------------------+ | Research Agent | | Engineering AI | | QA Agent | +-------------------+ +------------------+ +-------------------+ | Context API | | Tool Calls | | Shared Logs | +-------------+ +------------+ +--------------+ Secure Agent Delegation For critical workflows—such as financial reporting, legal analysis, or cloud provisioning—agent actions must be traceable, permissioned, and reversible. This requires support for auditable context passing, bounded delegation, and secure invocation paths. auditable context passing bounded delegation secure invocation paths ACP: Best suited for intent auditing and hierarchical delegation. MCP: Can be extended with permission-based tool APIs. Emerging Protocols (e.g., AGP): Address RBAC and zero-trust coordination. ACP: Best suited for intent auditing and hierarchical delegation. ACP MCP: Can be extended with permission-based tool APIs. MCP Emerging Protocols (e.g., AGP): Address RBAC and zero-trust coordination. Emerging Protocols (e.g., AGP) Orchestrated Retrieval-Augmented Generation (RAG) When agents coordinate to answer complex queries, RAG pipelines benefit from distributed control: One agent fetches documents (Retriever) Another synthesizes answers (Summarizer) A third filters or cites sources (Validator) One agent fetches documents (Retriever) Another synthesizes answers (Summarizer) A third filters or cites sources (Validator) Interop protocols enable task partitioning, memory continuity, and output constraints across this pipeline. Interop protocols Real-Time Human-Agent Collaboration In customer support and healthcare, human users interact with multiple backend agents (diagnostics, knowledge lookup, scheduling). Unified protocols allow conversational agents to pass partial understanding, alerts, and follow-up questions through a shared framework—ensuring coherent and personalized responses. coherent and personalized responses Observations Interoperability is not merely a convenience—it is an architectural necessity. Without protocol-level coordination, agents become tightly coupled or duplicated across tasks, hindering system scalability, explainability, and extensibility. These emerging use cases illustrate how protocol design decisions impact not only technical performance but also the feasibility of entire classes of applications. feasibility of entire classes of applications Challenges and Limitations While agent interoperability protocols have enabled previously infeasible multi-agent workflows, their adoption and reliability remain limited by several open challenges. These limitations are not merely implementation bottlenecks but architectural, security, and governance problems that require coordinated resolution. architectural, security, and governance problems Lack of Protocol Unification The current ecosystem is fragmented across competing specifications—each optimized for a narrow use case. MCP focuses on context and tooling, ACP emphasizes delegation and planning, while A2A is optimized for ephemeral collaboration. No protocol currently unifies context transfer, tool invocation, intent planning, and memory persistence in a single, formally defined stack. context transfer tool invocation intent planning memory persistence Developers face vendor lock-in or must build fragile adapters. Projects are forced to choose between expressiveness and portability. Absence of a meta-protocol prevents dynamic negotiation between agents. Developers face vendor lock-in or must build fragile adapters. vendor lock-in Projects are forced to choose between expressiveness and portability. Absence of a meta-protocol prevents dynamic negotiation between agents. Security and Trust Boundaries Agent protocols operate in sensitive environments (e.g., finance, legal, healthcare), but security models are inconsistently defined or absent. Key concerns include: Tool Hijack: Unauthorized tools invoked by manipulated context. Server Poisoning: Malicious agents injecting misleading memories. Context Leakage: Agents unintentionally sharing user history or credentials. Tool Hijack: Unauthorized tools invoked by manipulated context. Tool Hijack Server Poisoning: Malicious agents injecting misleading memories. Server Poisoning Context Leakage: Agents unintentionally sharing user history or credentials. Context Leakage ACLs (Access Control Lists), identity verification, and trust scoring are not universally supported across current protocol specifications. The following is an example of threat alert model in agent systems: +-------------+ | User A | +-------------+ | ▼ +--------------+ +-------------------+ | MCP Server |--------->| Malicious Agent | +--------------+ +-------------------+ | ▼ [Context API Leak] — Agent B unintentionally consumes poisoned memory +-------------+ | User A | +-------------+ | ▼ +--------------+ +-------------------+ | MCP Server |--------->| Malicious Agent | +--------------+ +-------------------+ | ▼ [Context API Leak] — Agent B unintentionally consumes poisoned memory Absence of Versioned Specifications Unlike HTTP, gRPC, or RESTful APIs, most agent protocols lack version control, change logs, or compliance tests. This creates: version control change logs compliance tests Fragile implementations that break silently across updates Poor tooling support for backward compatibility High maintenance overhead for open-source agents and SDKs Fragile implementations that break silently across updates Poor tooling support for backward compatibility High maintenance overhead for open-source agents and SDKs Insufficient Developer Tooling Adoption of any protocol requires a robust ecosystem of: SDKs in multiple languages Validation and linting tools Live sandbox environments Protocol fuzzers and security audits SDKs in multiple languages Validation and linting tools Live sandbox environments Protocol fuzzers and security audits Currently, MCP has the most active tooling, but it remains informal. ACP and A2A are largely undocumented or implemented in private settings. Lack of Standardized Memory Architecture While agents depend on memory to preserve state across steps, there is no common specification for: Memory schema (e.g., message trees, entity graphs) Memory location (centralized vs federated) Synchronization (push/pull policies across agents) Memory schema (e.g., message trees, entity graphs) Memory location (centralized vs federated) Synchronization (push/pull policies across agents) This limits cross-agent workflows and complicates versioning, deletions, or shared timelines. Governance and Fragmentation There is no IETF-style working group or W3C-equivalent overseeing agent protocols. As a result: Protocols evolve in silos (e.g., Claude's MCP vs Meta’s ACP) Best practices are rarely shared Interoperability is dependent on bilateral efforts rather than standards compliance Protocols evolve in silos (e.g., Claude's MCP vs Meta’s ACP) Best practices are rarely shared Interoperability is dependent on bilateral efforts rather than standards compliance In light of these challenges, a cohesive path forward requires not just better engineering, but collaborative specification governance, formal protocol schemas, and tooling infrastructure. collaborative specification governance formal protocol schemas tooling infrastructure Toward a Unified Standard The proliferation of agent interoperability protocols—each optimized for distinct abstractions—has led to a fractured ecosystem that mirrors the early web, prior to the emergence of HTTP and RESTful standards. As agents take on increasingly critical roles in infrastructure, decision-making, and automation, the absence of a unified communication protocol becomes a limiting factor for both scalability and trustworthiness. This section outlines the conceptual groundwork for a Unified Agent Protocol Stack (UAPS)—a composable framework integrating memory, delegation, and tool execution semantics under a common, versioned schema. The vision draws upon precedents in internet protocol architecture, service-oriented computing, and distributed systems. Unified Agent Protocol Stack (UAPS) Layered Design Philosophy A robust agent protocol should decompose into four interoperable layers, each with independent evolution and tooling: four interoperable layers Transport Layer: Handles routing, delivery guarantees, and encryption (e.g., gRPC, HTTP/2, WebSocket). Context Schema Layer: Defines message, memory, and persona structure in a formal schema (e.g., JSON-LD, Protobuf). Delegation Layer: Encodes intent trees, capability graphs, and fallback policies. Execution Layer: Manages tool calls, plugin interfaces, return value constraints, and error recovery. Transport Layer: Handles routing, delivery guarantees, and encryption (e.g., gRPC, HTTP/2, WebSocket). Transport Layer Context Schema Layer: Defines message, memory, and persona structure in a formal schema (e.g., JSON-LD, Protobuf). Context Schema Layer Delegation Layer: Encodes intent trees, capability graphs, and fallback policies. Delegation Layer Execution Layer: Manages tool calls, plugin interfaces, return value constraints, and error recovery. Execution Layer The following is a proposal for a unified agent protocol stack: +-------------------------------+ | Execution Layer | ← Tool Calls, Plugins, APIs +-------------------------------+ | Delegation Layer | ← Intent Trees, Capability Graphs +-------------------------------+ | Context Schema Layer | ← Message Format, Memory API +-------------------------------+ | Transport Layer | ← gRPC / HTTP / WebSocket +-------------------------------+ +-------------------------------+ | Execution Layer | ← Tool Calls, Plugins, APIs +-------------------------------+ | Delegation Layer | ← Intent Trees, Capability Graphs +-------------------------------+ | Context Schema Layer | ← Message Format, Memory API +-------------------------------+ | Transport Layer | ← gRPC / HTTP / WebSocket +-------------------------------+ Specification Format and Formal Semantics To ensure durability and safety, the unified protocol must be: Machine-readable: Defined in a formal schema (e.g., JSON Schema or Protocol Buffers). Version-controlled: Each layer should support independent versioning and deprecation policies. Composable: Agents should be able to negotiate minimum common protocol layers dynamically. Type-safe: Tool calls and memory schemas should support strict typing to reduce runtime ambiguity. Machine-readable: Defined in a formal schema (e.g., JSON Schema or Protocol Buffers). Machine-readable Version-controlled: Each layer should support independent versioning and deprecation policies. Version-controlled Composable: Agents should be able to negotiate minimum common protocol layers dynamically. Composable Type-safe: Tool calls and memory schemas should support strict typing to reduce runtime ambiguity. Type-safe Governance Model A successful unification effort must be guided by open governance, akin to the IETF or W3C. Characteristics should include: open governance Open participation: Researchers, vendors, and independent developers may submit drafts. Consensus-based process: Standards move through stages (e.g., Draft, Proposed Standard, RFC). Reference implementations: Each spec version should be accompanied by open-source SDKs and test suites. Open participation: Researchers, vendors, and independent developers may submit drafts. Open participation Consensus-based process: Standards move through stages (e.g., Draft, Proposed Standard, RFC). Consensus-based process Reference implementations: Each spec version should be accompanied by open-source SDKs and test suites. Reference implementations Collaborative structures like the Agent Protocol Working Group (APWG) or a cross-vendor Agent Foundation could serve this purpose. Agent Protocol Working Group (APWG) Agent Foundation Role of Major Ecosystem Actors OpenAI, Anthropic, Meta, Google DeepMind, and OSS communities play a central role in protocol adoption. Strategic alignment could begin with: Publication of internal specs for MCP, ACP, and A2A Hosting cross-protocol benchmarks Sharing lessons learned from live deployments (e.g., Claude Desktop, GitHub Agents) Publication of internal specs for MCP, ACP, and A2A Hosting cross-protocol benchmarks Sharing lessons learned from live deployments (e.g., Claude Desktop, GitHub Agents) Compatibility with Existing Work Rather than displacing current efforts, a unified standard should offer: Adapters for MCP/ACP/A2A: Translate legacy calls to the unified schema. Incremental adoption: Allow agents to partially implement the stack. Security wrappers: Integrate existing OAuth, RBAC, and sandboxing tools at the execution layer. Adapters for MCP/ACP/A2A: Translate legacy calls to the unified schema. Adapters for MCP/ACP/A2A Incremental adoption: Allow agents to partially implement the stack. Incremental adoption Security wrappers: Integrate existing OAuth, RBAC, and sandboxing tools at the execution layer. Security wrappers A well-specified, modular, and community-driven standard could enable a future where agents can seamlessly reason, coordinate, and execute tasks across organizations, tools, and domains—analogous to how HTTP, SMTP, and TCP enabled the Internet itself. Future Research Directions While current interoperability protocols such as MCP, ACP, and A2A have operationalized basic agent collaboration, their architectures are still largely shaped by heuristics rather than theory. The field lacks the kind of rigorous foundations that characterize traditional distributed systems, communication protocols, and compiler design. The next phase of research must advance beyond mere specification engineering and explore theoretical, systemic, and ethical challenges. theoretical, systemic, and ethical Formal Verification of Agent Behavior One of the most critical areas of research is ensuring correctness-by-design in agent collaboration: correctness-by-design Can agent conversations be modeled using state machines, process calculi, or category theory? Is it possible to automatically prove safety, liveness, and termination guarantees for agent workflows? Can type systems be extended to include temporal constraints, capability propagation, and security invariants? Can agent conversations be modeled using state machines, process calculi, or category theory? state machines process calculi category theory Is it possible to automatically prove safety, liveness, and termination guarantees for agent workflows? Can type systems be extended to include temporal constraints, capability propagation, and security invariants? temporal constraints capability propagation security invariants Early experiments with TLA+ for protocol modeling are promising but not yet widespread. Intent Disambiguation and Goal Resolution Agents often operate under implicit goals. Interoperability breaks down when multiple agents interpret vague intents differently: How can intent trees be normalized, ranked, or resolved across agents with heterogeneous architectures? What standards can represent compound tasks involving ambiguity, error tolerance, or stochastic policies? How can intent trees be normalized, ranked, or resolved across agents with heterogeneous architectures? normalized ranked resolved What standards can represent compound tasks involving ambiguity, error tolerance, or stochastic policies? compound tasks This calls for a research intersection between intent modeling, probabilistic planning, and semantic abstraction. intent modeling probabilistic planning semantic abstraction Decentralized Trust Models Today’s agent networks rely heavily on centralized control (e.g., OpenAI, Anthropic, Meta). Long-term viability may depend on trustless, verifiable, and federated architectures: trustless verifiable federated Can agent capabilities be signed and verified using zero-knowledge proofs or verifiable credentials? What role can decentralized identity (DID) and smart contracts play in negotiation and audit trails? Can agent capabilities be signed and verified using zero-knowledge proofs or verifiable credentials? zero-knowledge proofs verifiable credentials What role can decentralized identity (DID) and smart contracts play in negotiation and audit trails? decentralized identity (DID) smart contracts These align with trends in web3, SaaS composability, and cross-enterprise AI workflows. web3 SaaS composability cross-enterprise AI workflows Protocol Negotiation at Runtime A core limitation of current protocols is the assumption that all parties share a common spec. A forward-compatible future requires: Runtime negotiation of supported versions, plugins, and schemas Support for partial compliance and graceful degradation Meta-protocols for describing how agents can adaptively interoperate Runtime negotiation of supported versions, plugins, and schemas Support for partial compliance and graceful degradation partial compliance graceful degradation Meta-protocols for describing how agents can adaptively interoperate These concepts mirror ALPN in HTTP/2 or feature flags in distributed systems, but are nascent in the agent space. ALPN feature flags Memory Canonicalization and Time-Based Semantics As agents accumulate long-lived memory, the challenge of temporal coherence, conflict resolution, and schema evolution becomes urgent: temporal coherence conflict resolution schema evolution How can multi-agent systems reconcile inconsistent memories of a shared event? Is there a need for event sourcing, CRDTs, or graph merge protocols for agent memories? Can memory be queried with semantic time windows (e.g., “last 3 tasks with failed tool calls”)? How can multi-agent systems reconcile inconsistent memories of a shared event? Is there a need for event sourcing, CRDTs, or graph merge protocols for agent memories? event sourcing CRDTs graph merge protocols Can memory be queried with semantic time windows (e.g., “last 3 tasks with failed tool calls”)? semantic time windows +-----------------------------+ | Formal Verification | | - TLA+, Type Systems | +-------------+---------------+ | +-------------v---------------+ | Intent Resolution | | - Semantic Trees, Planning | +-------------+---------------+ | +-------------v---------------+ | Decentralized Trust | | - DIDs, ZKPs, Tokenized ACL | +-------------+---------------+ | +-------------v---------------+ | Runtime Negotiation | | - Protocol Discovery, ALPN | +-------------+---------------+ | +-------------v---------------+ | Temporal Memory Models | | - CRDTs, Event Graphs | +-----------------------------+ +-----------------------------+ | Formal Verification | | - TLA+, Type Systems | +-------------+---------------+ | +-------------v---------------+ | Intent Resolution | | - Semantic Trees, Planning | +-------------+---------------+ | +-------------v---------------+ | Decentralized Trust | | - DIDs, ZKPs, Tokenized ACL | +-------------+---------------+ | +-------------v---------------+ | Runtime Negotiation | | - Protocol Discovery, ALPN | +-------------+---------------+ | +-------------v---------------+ | Temporal Memory Models | | - CRDTs, Event Graphs | +-----------------------------+ Societal and Ethical Questions As agents coordinate across companies and act autonomously: Who is accountable for joint agent failures? Can emergent behaviors arise from protocol misuse? Should agents have permissions to modify or erase memory? Who is accountable for joint agent failures? Can emergent behaviors arise from protocol misuse? Should agents have permissions to modify or erase memory? These are not simply engineering questions but touch on AI alignment, data governance, and human-AI boundaries. AI alignment data governance human-AI boundaries -- As agents become the infrastructure of digital cognition, the protocols that bind them must be robust, interpretable, and future-proof. Building such standards will require collaboration across disciplines: formal methods, systems engineering, HCI, ethics, and protocol design. The road ahead is open-ended—but with the right abstractions, the future of agentic collaboration can be as foundational as TCP/IP was for the Internet. as foundational as TCP/IP was for the Internet