Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/endogenai/dogma/deterministic-agent-componentsgit clone --depth 1 https://github.com/EndogenAI/dogmaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/endogenai/dogma/deterministic-agent-components)<a href="https://agentmods.dev/agents/endogenai/dogma/deterministic-agent-components"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/deterministic-agent-components.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02100 |
| Opus 5 | $0.00000 | $0.01050 |
| Sonnet 5 | $0.00000 | $0.00420 |
| Haiku 4.5 | $0.00000 | $0.00210 |
Grade A, and why
deterministic-agent-components scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deterministic Components in LLM Agent Orchestration
Executive Summary
Pre-LLM chatbot architectures — AIML/Pandorabots, Rasa Core, Dialogflow CX, BotPress, and AWS Lex — converged independently on a structural split: a deterministic routing and decision layer, and a probabilistic content generation layer. This split is directly applicable to the EndogenAI agent fleet. A systematic mapping of the executive orchestrator workflow reveals that 63% of orchestration steps (12 of 19) are fully deterministic — script invocations, table lookups, file reads, git commands, state verifications — and require no language model inference. Extracting these steps to scripts and lookup tables reduces token burn, eliminates routing drift, and aligns with the Algorithms-Before-Tokens and Programmatic-First axioms. The recommended hybrid architecture — deterministic routing via an FSM + Delegation Decision Gate, with LLM reserved for synthesis, composition, and novel decomposition — is a direct application of the Rasa NLU/Core split to the endogenic fleet.
Hypothesis Validation
H1 — A Significant Fraction of Orchestrator Steps Are Deterministic
Verdict: CONFIRMED.
Mapping the 19 discrete steps in executive-orchestrator.agent.md (§1 Orient through
§6 Session Close) against a deterministic/LLM-required binary classification yields
12 deterministic steps (63%) and 6 LLM-required steps (32%), with 1 mixed. The
deterministic steps include: prune_scratchpad.py --init, reading scratchpad state,
consulting the Delegation Decision Gate table, dispatching a specialist agent,
confirming deliverable presence, running pre-review grep sweep, executing git commands,
running prune_scratchpad.py --force, and updating issue checkboxes. The LLM-required
steps — writing ## Session Start, writing ## Orchestration Plan, writing
## Pre-Compact Checkpoint, writing ## Session Summary, composing progress comments —
all involve synthesis and composition that justify LLM invocation. This 63% finding
aligns with BotPress production bot architecture empirics: in well-structured bots, the
majority of nodes are logic nodes; LLM nodes are the minority.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 170 lines · 0 tokens per session scan A 5ebe55e73dd7
deterministic-agent-components is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,100 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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