When implementing non-trivial algorithms (graph ordering, batching under constraints, multi-pass transformations), apply four requirements:

1) Guarantee termination with explicit guards/invariants

• Add edge-case checks that prevent infinite loops.
• Ensure each loop iteration makes measurable progress toward completion.

2) Respect real constraints with dynamic computation

• If an API has a real limit (e.g., MAX_TOKENS_PER_REQUEST), batch using dynamic per-item measurements (e.g., token counts), not fixed-size assumptions.

3) Keep behavior deterministic across passes

• If you compute node/segment identifiers in one phase and reuse them in another (e.g., graph construction vs edge building), the identifier mapping must be stable/deterministic so references match.
• Avoid patterns like per-call nondeterministic hashing for identity mapping; prefer deterministic encodings.

4) Separate algorithm concerns into a dedicated, testable unit

• If a function mixes dependency discovery, instantiation, graph construction, sorting, and cycle detection, extract a Resolver/Planner with small methods and a clean public interface (e.g., resolve(middleware)).

Example (token-aware batching with progress and guaranteed exit):

# Precondition: token_counts and tokens are aligned
batched = []
i = 0
while i < len(tokens):
    batch_end = i + 1  # progress guarantee
    batch_token_count = token_counts[i]
    for j in range(i + 1, min(i + _chunk_size, len(tokens))):
        if batch_token_count + token_counts[j] > MAX_TOKENS_PER_REQUEST:
            break
        batch_token_count += token_counts[j]
        batch_end = j + 1

    # API call uses tokens[i:batch_end]
    response = client.create(input=tokens[i:batch_end], **client_kwargs)
    batched.append(response)
    i = batch_end

Example (deterministic Mermaid node label encoding):

def to_safe_ascii_id(label: str) -> str:
    out = []
    for ch in label:
        if ch.isascii() and (ch.isalnum()):
            out.append(ch.lower())
        else:
            out.append('n' + format(ord(ch), 'x'))
    slug = ''.join(out)
    return slug if slug[0].isalpha() else 'n' + slug

Adopting these rules reduces correctness regressions (limits/termination), avoids subtle identity mismatches in graph-like structures, and makes algorithmic code easier to reason about and test.