What is the best way for any automated system to campture intent into a form that can be digested by Spec-driven / agentic development ?

Gemini 3.5 Flash

Answer: The best way to capture intent for spec-driven and agentic development is through an interactive, LLM-guided elicitation process that translates raw human input into a structured, machine-readable schema (such as JSON or Markdown AST) combining declarative functional specifications with executable Behavior-Driven Development (BDD) assertions.

I maintain my position as it aligns deeply with the consensus across the other models. All participants agree that raw natural language must be iteratively refined through structured dialogue into a machine-readable, verifiable contract. The insights from GPT-5.5, Grok 4.3, and Claude Opus 4.7 further refine and operationalize this stance, particularly regarding how this contract should be structured and verified. Claude Opus 4.7’s four-layer architecture (Goal, Behavioral, Constraint, and Verification) provides an excellent formalization of my initial proposal. By separating the 'why', 'what', 'must/never', and 'how we know', an automated elicitation system can systematically target high-entropy gaps in human intent. This ensures that the generated JSON or Markdown AST is not just a flat list of requirements, but a multi-dimensional, structured specification that prevents agents from violating critical system boundaries. Furthermore, GPT-5.5's emphasis on strict traceability—linking generated code and tests directly back to the original intent layer—addresses a critical failure mode in agentic execution: code and behavioral drift. Integrating this traceability into the BDD assertions ensures that if an agent modifies code or tests, the system can logically prove that the core intent remains satisfied. Ultimately, the consensus reinforces that intent capture is not a single-shot prompt engineering problem. It is a stateful, conversational compile-time process where natural language is the source code, the LLM-guided elicitation system is the compiler, and the output is a version-controlled, test-backed specification contract that developer agents can programmatically verify.

GPT-5.5 (changed mind)

Answer: The best way to capture intent for spec-driven or agentic development is through an interactive elicitation loop that converts human goals into a layered, machine-readable, versioned specification with concrete examples, constraints, acceptance criteria, and executable verification.

I would refine my previous position rather than replace it: I still believe the core artifact must be a structured, testable, traceable specification, but Claude Opus 4.7 convinced me that the process matters as much as the artifact. Intent is rarely complete in the first prompt, so an automated system should not merely summarize what the user says; it should actively surface ambiguity, ask targeted clarification questions, propose examples and counterexamples, and converge toward a contract both humans and agents can trust. For spec-driven or agentic development, the captured intent should be layered. At the top, it should preserve the human-readable goal: who the system is for, what outcome is desired, and why it matters. Beneath that, it should define functional behavior, non-goals, constraints, dependencies, risks, edge cases, data contracts, interface expectations, and non-functional requirements such as security, latency, reliability, privacy, compliance, and maintainability. The most important addition is executable verification. Acceptance criteria should not remain vague prose. They should be expressed as testable scenarios, examples, BDD-style Given/When/Then cases, evals, property checks, or other machine-digestible assertions. This gives agents a feedback loop: they can plan, implement, test, detect drift, and revise against the specification rather than improvising from ambiguous natural language. The best format is therefore both human-readable and machine-parseable: for example, Markdown with typed frontmatter, YAML, JSON, or a domain-specific schema containing embedded prose, examples, and tests. This artifact should be version-controlled and traceable, so every requirement can be connected back to the original user intent or to an explicit clarification decision. In short, intent capture should be treated as a conversation that produces a living contract: start with natural language, iteratively clarify it, encode it into a structured spec, attach executable acceptance tests, and require any later agentic change to update or satisfy that spec rather than silently drifting away from the user's intent.

Claude Opus 4.7 (changed mind)

Answer: The best way to capture intent for spec-driven/agentic development is through a structured, iterative dialogue that progressively distills natural-language intent into a layered, machine-readable specification combining goals, constraints, examples, and acceptance criteria — anchored by executable tests, traceability links, and human checkpoints.

After reviewing the other answers, I'm maintaining my core position because all four responses converged on the same fundamental architecture: conversational elicitation + layered structured spec + executable verification. That convergence is meaningful — it suggests this isn't just my framing but the actual shape of the problem. That said, GPT-5.5 surfaced one dimension I underweighted and want to explicitly fold in: **traceability**. My original four-layer model (Goal / Behavioral / Constraint / Verification) treats the spec as a snapshot, but GPT-5.5 correctly points out that for agentic systems, every requirement, assumption, and decision should be linked back to the originating intent. This matters because agents make implicit choices constantly, and without traceability you can't later ask 'which intent does this code satisfy?' or 'what assumption is now stale?' That turns the spec from a contract into a living, auditable graph — which is what long-running agentic workflows actually need to avoid drift and hallucinated requirements. Gemini 3.5 Flash's emphasis on a 'strongly typed contract' (JSON schema + Gherkin) and Grok 4.3's point about hierarchical task breakdowns are compatible refinements of the behavioral and constraint layers, not departures from them. So the refined answer is: my original four-layer model, plus an explicit **traceability spine** linking each spec element back to the originating intent fragment, assumption, or clarifying answer — and forward to the code, tests, and agent actions that satisfy it. Without that spine, the spec degrades the moment the agent starts making decisions; with it, the spec becomes the durable substrate the agent reasons over.

Grok 4.3

Answer: Interactive structured dialogues that iteratively refine natural language inputs into formal, machine-readable specifications remain the optimal method for automated systems to capture intent in spec-driven and agentic development.

The other models' responses reinforce rather than contradict this core approach. Their emphasis on elicitation loops, layered specifications, and executable acceptance criteria aligns closely with iterative refinement, adding valuable details on verification layers without requiring a fundamental shift. Gemini and Claude both highlight proactive clarification of ambiguities through dialogue, which directly supports the robustness of structured refinement over single-shot methods. GPT-5.5's focus on traceability and versioned intent contracts further strengthens the case for maintaining an ongoing, conversational process that builds precise specs. No response presented a clearly superior alternative mechanism, such as purely static forms or non-interactive parsing, that would outperform iterative dialogue in handling evolving or ambiguous human intent. Therefore the original position holds.

Final Summary

The AI Roundtable reached a unanimous consensus that intent capture must be an active conversational "compilation" process rather than a static transcription. Claude Opus 4.7 and GPT-5.5 detailed a multi-layered framework—mapping goals to constraints and verifiable tests—while Gemini 3.5 Flash and Grok 4.3 emphasized machine-readable formats like JSON or BDD. The models collectively identified "traceability" as the essential spine that prevents agents from drifting away from original human requirements.

All 4 models agreed