[GitHub] Implement a governance layer for AI workflows

Hi everyone, I’ve been building an open-source project called REBIS, and I wanted to share it here because I think it sits in an interesting place between systems design, AI workflow infrastructure, and the philosophy of reasoning over time. Repo: [https://github.com/Nefza99/Rebis-AI-auditing-Architecture](https://github.com/Nefza99/Rebis-AI-auditing-Architecture) At a practical level, REBIS is an experimental governance runtime for long-horizon AI agent workflows. But at a deeper level, the problem I’m trying to explore is this: How does a reasoning process remain the same reasoning process across many transitions? That might sound abstract at first, but I think it points to a very concrete failure mode in modern AI systems. The problem that led to REBIS A lot of current AI workflows increasingly rely on: \- multi-step reasoning \- repeated tool use \- agent-to-agent handoffs \- planning → execution → revision loops \- proposal / merge cycles \- compressed state passing through summaries or partial context In short chains, these systems can look quite capable. But as the chain gets longer, the workflow often starts to degrade in ways that seem deeper than simple one-step output errors. The kinds of problems I kept noticing or thinking about were things like: \- reasoning drift \- dropped constraints \- mutated assumptions \- corrupted handoffs \- repeated correction loops \- detached provenance \- wasted computation spent repairing prior instability What struck me is that these failures often seem cumulative rather than instantaneous. The workflow does not necessarily collapse because one step is wildly wrong. Instead, it seems to lose integrity gradually, until the later steps are no longer faithfully pursuing the same objective the workflow began with. That intuition became the foundation of REBIS. The philosophical core Most orchestration systems assume continuity of purpose. If an agent hands work to another agent, or calls a tool, or receives a summary of prior state, the system generally proceeds under the assumption that the workflow remains “about” the same task. But I’m not convinced that continuity should be assumed. I think it often needs to be governed. Because a workflow is not only a chain of actions. It is a chain of state transformations that implicitly claim continuity of reasoning. And if those transformations are lossy, slightly distorted, or structurally inconsistent, then the system may still be producing outputs, still calling tools, still appearing active — while no longer, in a deeper sense, being engaged in the same reasoning process. That is the philosophical problem underneath the engineering one: When does a workflow stop being the same thought? To me, that is not just a poetic question. It has direct computational consequences. A mathematical intuition: reasoning states The way I started trying to formalize this was by treating a workflow as a sequence of reasoning states: S₀, S₁, S₂, S₃, ..., Sₙ where: \- S₀ is the original objective state \- Sᵢ is the reasoning state after transition i Each transition can be represented as an operator: Sᵢ₊₁ = Tᵢ(Sᵢ) where Tᵢ could correspond to: \- an agent reasoning step \- a tool invocation \- an agent handoff \- a summarization step \- a proposal merge \- a retry / repair cycle This is useful because it shifts the focus from “did the model answer correctly once?” to a more systems-oriented question: What happens to the integrity of state across workflow depth? Defining drift From there, drift can be defined as the difference between the current reasoning state and the original objective state: Dᵢ = d(Sᵢ, S₀) where d(·,·) is some distance, mismatch, or divergence measure. I’m intentionally leaving d somewhat abstract because I think different implementations could instantiate it differently: \- embedding-space distance \- symbolic constraint mismatch \- provenance inconsistency \- contract violation count \- output-structure deviation \- hybrid state divergence metrics The exact metric is less important than the systems intuition: \- if Dᵢ stays small, the workflow remains aligned \- if Dᵢ grows, the workflow is drifting away from the original objective At the start: D₀ = 0 and ideally, for a stable workflow, accumulated drift remains bounded. Why long workflows fail gradually A simple way to think about incremental degradation is: δᵢ = Dᵢ₊₁ - Dᵢ where δᵢ is the deviation introduced by transition i. Then cumulative drift after n steps can be thought of as: Dₙ = Σ δᵢ This is the key insight I’m exploring: Long-horizon workflow failure is often cumulative rather than instantaneous. No single transition necessarily “breaks” the system. Instead, the workflow undergoes a series of locally plausible mutations, and eventually the total divergence becomes large enough that the output is no longer faithfully solving the original task. In that sense, the problem resembles issues of identity and continuity: there may be no single dramatic break, and yet the process is eventually no longer the same process. In engineering terms, that is simply drift accumulation. Why this is not only a correctness problem The more I thought about it, the more it seemed like drift is not just about correctness. It is also about compute allocation. Because once drift accumulates, the system often has to spend more cycles correcting itself: \- recovering dropped constraints \- restoring context \- repairing invalid handoffs \- retrying failed transitions \- reissuing equivalent tool calls \- re-anchoring to the original objective So total computation can be decomposed as: C\_total = C\_progress + C\_repair where: \- C\_progress = compute used to advance the actual objective \- C\_repair = compute used to correct accumulated workflow instability A simple hypothesis is: C\_repair ∝ Dₙ That is, as accumulated drift increases, repair overhead increases. This gives the practical causal chain: drift ↑ ⇒ repair overhead ↑ ⇒ useful progress per unit compute ↓ And inversely: drift ↓ ⇒ repair overhead ↓ ⇒ useful progress share ↑ That’s one of the reasons I think this is an important systems problem. If the same compute budget can be spent on more actual progress and less downstream repair, then the value of governance is not only stability or safety. It is also better results from the same computational budget. What REBIS is trying to do REBIS is my attempt to explore that missing layer as an open-source project. The basic idea is: instead of workflows behaving like this: Agent → Agent → Tool → Agent → Merge → Agent REBIS inserts a governance layer between transitions: Agent → REBIS runtime → validated transition → next step The core idea is not to make agents endlessly self-reflect inside their own loops. It is to move transition integrity outward into runtime structure. In simple terms: \- agents perform reasoning and tool use \- REBIS governs whether the workflow can validly proceed What the runtime governs The architecture I’m exploring revolves around a few key primitives. 1. Transition validation Every transition should be checked for things like: \- objective alignment \- hard constraint preservation \- required state completeness \- valid handoff structure \- expected output shape \- optional drift threshold conditions Possible outcomes are explicit: \- approve \- repair \- reject \- escalate That matters because a transition should not be allowed to proceed just because it looks superficially plausible. It should proceed only if it preserves enough of the workflow’s integrity. 2. Policy-bound reasoning contracts One of the main concepts in REBIS is the idea of reasoning contracts. A reasoning contract defines what must remain true before a workflow step may continue. For example, a contract might specify: \- objective anchor what task or subgoal this step must still serve \- hard constraints conditions that must not be dropped, weakened, or mutated \- required state context that must already exist before the transition is valid \- allowed actions permissible categories of next steps \- expected output structure the form the result must satisfy \- failure policy whether violation should trigger repair, rejection, escalation, or replanning This shifts the runtime from vague “monitoring” toward something more formal: valid(Tᵢ(Sᵢ), Cᵢ) = true / false In other words, each step is not only executed. It is evaluated against a structured condition of valid continuation. 3. Task-state ledger REBIS also treats workflow state as runtime-owned. Instead of letting agents act as the sole carriers of context, the runtime maintains a task-state ledger that can track: \- objective \- constraints \- current plan \- completed work \- remaining work \- outputs \- transition history \- contract history \- repair events \- drift events This matters because many long-horizon failures seem to happen when downstream components inherit incomplete or distorted state and then spend compute reconstructing intent from compressed summaries. A runtime-owned ledger is an attempt to reduce that reconstruction burden. 4. Boundary-local repair Another important design principle is that if a transition is bad, the system should prefer to repair the boundary rather than rerun the whole workflow. For example: \- if a handoff loses a constraint, repair the handoff \- if required state is missing, restore it locally \- if the output shape is invalid, repair or reject that transition \- if drift crosses a threshold, re-anchor before continuing This is important for both correctness and compute efficiency. Local repair is often cheaper than broad reruns. 5. Observability If this is going to be a real systems layer, it needs observability. So REBIS is also oriented toward runtime visibility into things like: \- drift events \- rejected transitions \- repair counts \- loop detections \- redundant tool calls \- reused cached steps \- transition lineage \- incident-review traces Otherwise it becomes difficult to tell whether governance is actually improving the workflow or simply adding complexity. Bounded drift as the runtime goal The cleanest mathematical way I’ve found to express the runtime objective is something like: Dₙ ≤ B for some acceptable bound B. That is, REBIS is not trying to force perfect immutability. It is trying to keep drift bounded enough that the workflow remains recognizably engaged in the same task. That leads to a compact optimization framing: Minimize Dₙ subject to preserving workflow progress or more fully: Minimize Dₙ and C\_repair while maximizing task fidelity That, to me, is the strongest concise mathematical statement of the REBIS idea. Why I think this may matter as open-source infrastructure There are already many good open-source tools for: \- model access \- task orchestration \- graph execution \- retries \- tool integration \- distributed compute What I’m less sure exists in a mature way is a layer for: runtime governance of reasoning progression across workflow depth Not just: \- what runs next \- which agent is called \- which tool executes But: \- whether the workflow is still the same reasoning process it began as \- whether transition integrity remains intact \- whether accumulated drift is being controlled \- whether compute is being preserved for useful progress instead of repair churn That’s the open-source direction I’m trying to explore with REBIS. The hypothesis in its simplest form The strongest compact version of the hypothesis is: Dₙ ↓ ⇒ C\_repair ↓ ⇒ C\_progress / C\_total ↑ ⇒ task fidelity ↑ In words: If governed transitions keep accumulated drift smaller, then repair overhead stays smaller, more of the compute budget goes toward useful progress, and final task fidelity should improve. That is the reason I think the problem is worth formalizing. Why I’m posting this here I’m sharing it on r/github because I’m building this openly and I’d genuinely value feedback from people who think about: \- open-source systems \- AI infrastructure \- workflow runtimes \- orchestration layers \- stateful agent systems \- long-horizon reliability I’m not attached to the terminology. I’m attached to the problem. I’m currently building REBIS as an experimental runtime to explore whether governed transitions, reasoning contracts, and task-state preservation can reduce accumulated drift and wasted computation in long-horizon AI workflows. If this problem space is interesting to you, or if you’re working on something similar, feel free to reach out. Thanks for reading.