Prompt-Free Collaborative Agent Framework

Updated 9 December 2025

Prompt-Free Collaborative Agent Framework is a system that coordinates multi-agent efforts without dynamic prompt engineering, using pre-specified guidelines and structured exchanges.
It relies on fixed system messages, standardized communication protocols, and self-reflexive decision processes to ensure reliable, interpretable collaboration.
Applications span dialogue management, scientific automation, recommendations, and creative content generation, demonstrating improvements in scalability and task efficiency.

A prompt-free collaborative agent framework is a system architecture for multi-agent collaboration in which coordination, information transfer, and task refinement are achieved without explicit, ad hoc, or per-task prompt engineering. Instead, these frameworks rely on pre-specified system-level guidelines, fixed system messages, structured state or workspace exchanges, and autonomously emergent communication for robust and scalable multi-agent operation. Prompt-free designs enable reliable, extensible, and interpretable collaboration across diverse domains including dialogue management, scientific automation, creative content generation, recommendation, workflow execution, code generation, and search. They stand in contrast to prompt-based orchestrations, which require hand-crafted or dynamically generated language instructions to steer agent behavior at runtime.

1. Architectural Principles of Prompt-Free Collaborative Agent Frameworks

Core architectural strategies in prompt-free collaborative frameworks include:

delegated, modular agency with clear role separation,
structured inter-agent communication (e.g., knowledge bases, workspaces, or standardized message schemas),
autonomous, self-reflexive decision processes (without hard-coded prompt chains or stepwise user input), and
explicit separation of natural language generation from core reasoning or state management.

For instance, in frameworks utilizing dual-agent (Manager–Assistant) architectures with logic-grounded reasoning, such as in fast-food order management, agents interface exclusively via a shared Answer Set Programming (ASP) knowledge base with encapsulated predicates and collaborative rule sets, entirely avoiding natural-language prompt exchange between agents (Zeng et al., 9 May 2025). In scientific research automation frameworks, modular multi-agent collectives communicate through file-based workspaces and serialized JSONL logs, coordinated by a central manager that reasons over standardized tool outputs—again eschewing prompt cascades or handovers (Li et al., 17 Oct 2025).

A cross-section of principal architectural elements is shown below:

Framework	Coordination Mechanism	Communication Channel	Reasoning Core
Logic–Based	Shared ASP KB, atomic rules	Predicates, update lists	Logic programming
Workspace–Based	File/dir tree, logs	JSONL, file refs, summaries	Manager/worker LLMs
Message-Passing	Typed minimal schemas	JSON-like inter-agent calls	Agent-specific LLMs/tools
Stateful Search	Dialogue history + context	Limited fixed templates	LLM with few-shot examples

Frameworks such as CoSearchAgent further reinforce prompt-freedom by encoding all task roles (query rewriting, clarification, extraction, retrieval-augmented generation) as internal system messages and chain-of-thought-enhanced prompts within a fixed context, with no runtime prompt alteration (Gong et al., 9 Feb 2024).

2. Internal Communication, Knowledge, and Grounding

Prompt-free frameworks fundamentally decouple communication semantics from natural-language instructions. Inter-agent state transfer occurs via:

Structured knowledge bases (e.g., ASP facts/rules/predicates (Zeng et al., 9 May 2025));
File-based or workspace APIs (e.g., predictable tool actions, agent logs, and persistent memory via files and JSONL entries (Li et al., 17 Oct 2025));
RPC-like message protocols with strongly-typed payloads (e.g., {from, to, type, payload} schemas in recommendation and orchestration systems (Wang et al., 23 Feb 2024)).

These mechanisms facilitate:

atomicity (all-or-nothing knowledge/base updates),
encapsulation (limited visibility—agents see only necessary state, not full KB),
immutable traceability (logs, transaction records),
consistency verification (reasoning layers validate all suggested state changes against constraints prior to integration).

Agents operate on the formal representations at their interface while using LLMs strictly for semantic parsing, clarification, and language generation—not reasoning or inter-agent communication per se (Zeng et al., 9 May 2025, Li et al., 17 Oct 2025).

Autonomous task execution and iterative improvement, without runtime prompt engineering, are core to these frameworks. Notable design patterns include:

Verification–Refinement Loops: In automated code reproduction, a Verification Agent checks output completeness against original system prompts, while a Refinement Agent amends deficiencies using only the same prompts, both realized as LLM calls with fixed templates (Lin et al., 2 Dec 2025). This ensures alignment between objective, verification, and refinement, eliminates negative transfer from ad hoc prompts, and bolsters robustness across heterogeneous tasks.
Alignment-Driven Workflow: In collaborative work frameworks (e.g., Communication to Completion/C2C), agents maintain an Alignment Factor $AF_{i,j}$ per subtask, updated via in-context LLM judgments on peer communication. Productive effort is modulated by $AF_{i,j}$ , directly incentivizing targeted, high-yield communication and minimizing cost (Lu et al., 22 Oct 2025). Communication choices emerge from a sequential action framework, mapped from LLM-based intention prompts.
Dynamic and Modular Orchestration: Manager agents in scientific automation and recommendation frameworks reason over real-time system reports, summary files, and structured feedback (Peer Review, Reflection, Task Interpretation), choosing next steps entirely at their discretion and via workspace state, without pre-specified prompt triggers (Li et al., 17 Oct 2025, Wang et al., 23 Feb 2024).

Algorithmic sketches for these processes (as in (Lin et al., 2 Dec 2025, Wang et al., 23 Feb 2024)) clarify their independence from prompt chains:

O_i = LLM.generate(system_prompt)
R_i = VerificationAgent(O_i, S_i, P)
if R_i.missing_information:
    O_i_star = RefinementAgent(O_i, R_i, S_i, P, prev_O_star)
else:
    O_i_star = O_i

4. Evaluation and Empirical Benchmarks

Prompt-free collaborative frameworks have been evaluated across dialogue, code generation, recommendation, research automation, and creative generation tasks:

Automated code reproduction benchmarks show ~15% improvement in accuracy and completeness versus non-collaborative baselines—improvements are attributed to systematic alignment and the removal of hand-tuned refinement prompts (Lin et al., 2 Dec 2025).
In team-based code workflows, prompt-free frameworks led to ~40% reductions in completion time over fixed-step or non-communicative baselines, maintaining total success and significant scalability, attributed to dynamic, AF-driven communication control (Lu et al., 22 Oct 2025).
Recommendation system evaluations demonstrate relative gains of 5–12% in metrics such as NDCG@10 and RMSE, with prompt-free multi-agent collaborations outperforming single-agent approaches in all tested modalities (Wang et al., 23 Feb 2024).
Creative image editing saw improvement in quantitative metrics such as CLIP Score, LPIPS, and VENDI over baseline prompt-based methods (Venkatesh et al., 7 Apr 2025).
Dialogue realism, user satisfaction, and reliability metrics in human–agent collaborative settings consistently favored prompt-free agent systems (as measured in side-by-side human evaluations (Zeng et al., 9 May 2025, Shao et al., 20 Dec 2024)).

The table below summarizes the outcomes for selected tasks:

Domain	Key Metric	Relative Gain (%)	Reference
Code Repro	Completeness/Accuracy	13–15	(Lin et al., 2 Dec 2025)
Recommendation	NDCG@10	13 (0.287→0.324)	(Wang et al., 23 Feb 2024)
Workflow	Completion Time	~40 (complex tasks)	(Lu et al., 22 Oct 2025)
Dialogue	Total Satisfaction	+0.88 (9.00 vs 8.12)	(Zeng et al., 9 May 2025)

Results suggest that prompt-free design avoids the pitfalls of prompt overfitting and negative transfer, generalizes better across data and tasks, and supports modular extension.

5. Limitations and Research Challenges

Identified limitations include:

Requirement for up-front modeling of structured communication interfaces (e.g., predicate vocabularies, workspace tools, role definitions) (Zeng et al., 9 May 2025).
Latency bottlenecks due to LLM parsing and generation when not optimized at scale.
Token-length constraints in context windows, managed via memory compaction, truncation, or context persistence (Gong et al., 9 Feb 2024, Li et al., 17 Oct 2025).
The need for explicit integrity constraints and atomic update rules to prevent drift or knowledge base inconsistency.
Difficulty in engineering situational awareness, fine-grained autonomy control, and avoidance of repetitive/error-prone strategies across extended multi-agent interactions (Shao et al., 20 Dec 2024).

Potential challenges also arise in scaling to more complex, concurrent multi-agent settings where reasoning over distributed, partial, or asynchronous knowledge becomes critical.

6. Applications and Extensibility

Prompt-free collaborative agent paradigms have been instantiated in several high-impact domains:

Task-Oriented Dialogue: Modular LLM + logic agents for domain-specific customer–manager communication, defeating standard end-to-end LLM bots in human evaluation (Zeng et al., 9 May 2025).
Automated Science and Research: Open, continually running agent-based research groups managing ideation, experimentation, writeup, and peer review with workspace-based coordination (Li et al., 17 Oct 2025).
Information Retrieval and Search: Lightweight collaborative querying modules for chat-centric platforms, supporting flexible, contextually grounded responses (Gong et al., 9 Feb 2024).
Recommendation Tasks: Dynamic agent teams for sequential, conversational, and explanation generation recommendation with consistently superior performance (Wang et al., 23 Feb 2024).
Creative Content Generation: Multi-agent image synthesis/editing pipelines for creativity-driven tasks, leveraging autonomous role decomposition and iterative, agentic self-improvement (Venkatesh et al., 7 Apr 2025).

The frameworks are extensible to further use-cases by substituting new role definitions, tool stacks, and domain-specific action schemas without altering the core collaborative mechanism, provided the structured interfaces and reasoning protocols are respected.

7. Design Guidelines and Best Practices

Best practices synthesized across frameworks emphasize:

Defining closed, well-documented predicate/action/tool vocabularies in advance (Zeng et al., 9 May 2025, Li et al., 17 Oct 2025).
Restricting LLMs to parsing and generation roles, with all logic, state management, and consistency checking delegated to formal systems (e.g., ASP, workspace state, or agent reasoning loops).
Committing to atomic, integrity-checked updates in state exchanges.
Encapsulating and partitioning knowledge to minimize accidental information exposure.
Using unified, minimal schemas for message passing, stateless orchestration, and file/tool-based communication.
Systematic validation of all agent outputs before further workflow progression.
Modularizing shared logic for agent-independent reasoning and scalability.

By adhering to these principles, prompt-free collaborative agent frameworks achieve reliability, extensibility, explainability, and measurable improvements in collaborative task performance, establishing themselves as foundational infrastructure for robust multi-agent systems in both research and industrial automation contexts (Zeng et al., 9 May 2025, Li et al., 17 Oct 2025, Lin et al., 2 Dec 2025, Gong et al., 9 Feb 2024, Wang et al., 23 Feb 2024, Venkatesh et al., 7 Apr 2025).