Agentic Coding Manifests

Updated 14 November 2025

Agentic coding manifests are formal artifacts that define operational principles, agent roles, and workflow structures for autonomous software systems.
They use structured formats like Markdown, YAML, and rule sets to provide reproducible context and clear action delegation in AI-driven development.
Empirical studies show that manifest-driven architectures enhance merge rates, improve security standards, and streamline autonomous code generation.

Agentic coding manifests are formal artifacts—specifications, configuration files, workflow blueprints, or theoretical frameworks—that regulate, orchestrate, and document the operation and reasoning of autonomous AI agents in software engineering contexts. They stipulate principles, action schemas, feedback mechanisms, operational roles, and alignment strategies for agent-driven programming systems. Current research frames these manifests as indispensable in establishing project context, operational constraints, agent responsibilities, verification protocols, and meta-cognitive routines essential to robust, scalable, and trustworthy autonomous code generation and software delivery pipelines.

1. Conceptual Foundations and Definitions

Agentic coding is defined as the paradigm in which high-level software development tasks are delegated to autonomous AI agents which, in contrast to prompt-driven or "vibe coding" approaches, perform planning, execution, testing, and refinement with minimal human intervention (Sapkota et al., 26 May 2025). Manifests in this context are structural or formal artifacts—sometimes Markdown files (e.g., CLAUDE.md), YAML/JSON rule sets (as in security manifests), or mathematical blueprints—ingested by agentic systems to determine project intent, action constraints, and the division of labor within human-agent ecosystems (Chatlatanagulchai et al., 18 Sep 2025, Hoda, 22 Oct 2025).

Agentic coding manifests may take the form of:

Markdown manifests (e.g., CLAUDE.md): Define operational and architectural instructions for agents on real-world projects (Chatlatanagulchai et al., 18 Sep 2025).
Workflow blueprints: Systematize sub-agent responsibilities, e.g., code generation, test generation, repair, and style enforcement (Saul et al., 8 Jun 2025).
Formal reward specifications, architectural diagrams, or acceptance rule definitions within reinforcement learning-based agents (Li et al., 13 Oct 2025).
Probabilistic or mathematical structures specifying utility-composability or alignment properties among subagents (Lee et al., 8 Sep 2025).

These artifacts underpin agentic coding systems by supplying persistent context, encoding guiding principles, and enabling reproducibility across codebases and research efforts.

2. Structural Patterns and Content of Manifests

Empirical examinations of ACMs, notably in (Chatlatanagulchai et al., 18 Sep 2025), reveal a systematic preference for shallow, action-oriented hierarchies. Across 253 CLAUDE.md files, manifests exhibit the following median structural properties:

Heading Level	Median Count
H1	1.0
H2	5.0
H3	9.0

Sections rarely extend beyond H3, and deep nesting is discouraged, optimizing for agent (and human) accessibility and rapid workflow localization. The dominant content categories—"Build and Run" (77.1%), "Implementation Details" (71.9%), and "Architecture" (64.8%)—align with actionable, technical instructions and high-level system structure, as detailed below:

Category	Frequency	Description
Build and Run	0.771	Compile/run/test invocation details
Implementation Details	0.719	Language versions, conventions, helper scripts
Architecture	0.648	Module maps, data flow, deployment targets

Additional fields often include explicit agent role definitions, test procedures, environment setup, and links to artefacts or documents (Chatlatanagulchai et al., 18 Sep 2025). In security-focused systems, manifests are typified by flat mappings of threat patterns (CWEs) to enforceable guidelines (Saul et al., 8 Jun 2025). This structural minimalism and focus on actionable knowledge is demonstrated to increase efficiency, reduce ambiguity, and create uniform expectations.

3. Formalization: Mathematical and Algorithmic Manifests

Agentic coding manifests are increasingly formalized to encompass reward equations, acceptance rules, and policy constraints. For instance, in ReLook (Li et al., 13 Oct 2025), the generate–diagnose–refine architecture is encoded via:

A trajectory-based reward:

$R_{\text{ReLook}}(o) = R_{\text{MLLM}}(o) \times R_{\text{len}}(o)$

Where $R_{\text{MLLM}}$ is a visual-fidelity score from a multimodal LLM critic, and $R_{\text{len}}$ is a token penalty anchored by code length thresholds.

Strict "Forced Optimization" acceptance criteria:

$s_r > \max_{i<r}s_i,\quad \forall r$

Ensures monotonic improvement and prevents regression across iterations.

Pseudocode or workflow algorithms specifying sub-agent orchestration (e.g., SCGAgent's stepwise application of security guidelines and automatic test loops (Saul et al., 8 Jun 2025)).

Probabilistic agentic manifests encode modularity and compositionality of agent reasoning. Subagents are modeled as $P_i: \mathcal{O} \rightarrow [0,1]$ , and their aggregation obeys log-pooling:

$P_\alpha(o) \propto \prod_{i=1}^n P_i(o)^{\alpha_i}$

This ensures compositional welfare improvement (welfare gap $\Delta_i \ge 0$ for all $i$ ) under log-pooling, but not under linear pooling or in binary outcome spaces (Lee et al., 8 Sep 2025).

These mathematical formalizations establish precise, algorithmically verifiable contracts for agent decision-making, credit assignment, and alignment.

4. Manifest-Driven Architectures, Workflows, and Tooling

Agentic coding manifests regulate end-to-end workflows, from developer intent to deployment. Architectures described in (Sapkota et al., 26 May 2025) and (Roychoudhury, 24 Aug 2025) formalize modular loops with distinct agent roles:

Planner: Parses user goal, produces task graphs.
Executor: Executes code transformations per subtask.
Tester: Runs integrated verification.
Feedback Agent: Analyzes results, triggers replanning or rollback.

An agentic workflow template:

function AgenticAgent(task):
    code = gen_code(task)
    tests = gen_tests(task)
    code, tests = enforce_func(task, code, tests)
    cwe_list = predict_cwe(task, code)
    guidelines = lookup_guidelines(cwe_list)
    for guideline in guidelines:
        if not check_relevance(task, code, guideline):
            code = guided_modify(task, code, guideline)
            code, tests = enforce_func(task, code, tests)
    return code

(Saul et al., 8 Jun 2025)

Manifests coordinate these components via rules for action delegation, eligible tool invocations, and domain-specific policy enforcement. The Lita system demonstrates that, when agentic manifests are minimized (few tools, low prompt token cost), strong LLMs reveal their raw capabilities, and auxiliary scaffolding becomes increasingly redundant (Dai et al., 30 Sep 2025).

5. Evaluation, Empirical Performance, and Best Practices

Empirical studies (Chatlatanagulchai et al., 18 Sep 2025, Saul et al., 8 Jun 2025, Watanabe et al., 18 Sep 2025) and agentic coding blueprints emphasize the following practices for manifest creation and evaluation:

Shallow, action-oriented document hierarchies improve agent throughput and codebase navigation.
Explicit enumeration of operational rules (compilation, testing, CI/CD interactions) increases the merge rate of agentic pull requests: Agentic-PRs on GitHub show an overall acceptance rate of 83.8%, with 54.9% direct merges (Watanabe et al., 18 Sep 2025).
Externalization of coding policies—such as security guidelines in manifest files—allows scalable and auditable enforcement with minimal drift from baseline model behavior, yielding security improvements of up to +25% with only ∼2% decrease in function pass rates (Saul et al., 8 Jun 2025).
The Agent Complexity Law (Dai et al., 30 Sep 2025): As LLM capability grows, the marginal benefit of complex agentic scaffolding vanishes, advocating for minimal manifest and tool schemas for strong agents; explicit editing and reasoning tools remain useful for weaker models.
Quantitative metrics include pass rates, test adherence, efficiency (average tool steps, token consumption), and cost; these are standard in benchmarking agentic systems and their manifest-driven workflows (Dai et al., 30 Sep 2025).

6. Socio-Technical Scope, Values, and Future Principled Development

Contemporary manifestos extend the agentic coding manifest to encode not only workflow or action schemas, but also values, ethics, and cross-phase applicability. The CRAFT framework (Hoda, 22 Oct 2025) specifies that agentic coding must be:

Comprehensive: Spanning all software process areas.
Responsible: Embedding ethics-by-design and sustainability constraints.
Adaptive: Aligned to evolving SOTA models and socio-technical impact.
Foundational: Supported by theory, taxonomy, and validated empirical results.
Translational: Centered on actionable artifacts and gradual awareness-raising.

Formally, agentic processes are governed by matrices of human–agent oversight, value-constrained optimization of utility, and phase-bridging templates such as the Agentic Coding Canvas. This aligns the technical scopes of manifests (reward, workflow, action schema) with broader organizational, social, and ethical requirements in software engineering (Hoda, 22 Oct 2025).

7. Probabilistic and Alignment-Theoretic Manifests

Probabilistic modeling exposes the subagent structure of deep neural models, providing a manifesto for agentic alignment engineering. The key implication is that modular subagents can be composed via log-pooling to jointly maximize expected epistemic utility, subject to compositionality checks (ensuring welfare improvement for all subagents) (Lee et al., 8 Sep 2025). This mathematical framework offers explicit API patterns:

Subagent interface: export distribution and logit profile
LogPool class: aggregate subagents and monitor utility gaps
Alignment pipeline: manifest helpful/antagonistic personas, adjust subagent weights under KL constraints, monitor harmful direction shattering

This theoretical underpinning facilitates agentic coding manifest design that is reliably modular, interpretable, and resistant to emergent misalignment phenomena.

Agentic coding manifests thus serve as the canonical locus of operational, architectural, and ethical knowledge for autonomous software engineering agents. Their continued refinement—in content, formalism, and best-practice implementation—is central to scaling reliable, responsible, and efficient agent-driven programming systems across increasingly complex software lifecycles.