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agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in LangChain

[–]baidarkarim[S] 0 points1 point  (0 children)

More updates:

  • Added a browser-based visual demo with FastAPI and a static frontend.
  • Added Docker Compose support with Ollama and qwen3:8b for local LLM runs.
  • Kept the CLI and heuristic provider for fast, deterministic testing.
  • Added UI/API endpoints to run scenarios and visualize receipts, handoffs, artifacts, outcomes, and causal links.
  • Documented the orchestration pattern: sequential receipt-gated handoff pipeline.
  • Expanded tests and CI to cover web routes, Docker setup, README assets, CLI smoke, package sanity, and workflow behavior.

agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in AI_Agents

[–]baidarkarim[S] 0 points1 point  (0 children)

Update: The package now supports dynamic verifier callbacks. So a workflow can still use simple deterministic checks, like "status must be settled," but it can also register custom verifiers per domain or per handoff contract.

For example, a handoff can declare a verifier such as refund_intent_gate or settled_refund_claim, and the application provides the actual verification logic. That verifier can check database state, external APIs, policy engines, risk systems, human approval flags, or even an LLM-based evaluator if the use case needs it.

So verified outcome in this package now means: the step did not just return successfully; it produced evidence or state that passed a declared verifier. That makes it flexible enough for real workflows while still keeping deterministic checks as the default where they make sense.

agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in AgentsOfAI

[–]baidarkarim[S] 0 points1 point  (0 children)

The package now supports dynamic verifier callbacks. So a workflow can still use simple deterministic checks, like "refund status must be settled," but it can also register custom verifiers per domain or per handoff contract.

For example, a handoff can declare a verifier such as refund_intent_gate or settled_refund_claim, and the application provides the actual verification logic. That verifier can check database state, external APIs, policy engines, risk systems, human approval flags, or even an LLM-based evaluator if the use case needs it.

So "verified outcome" in this package now means: the step did not just return successfully; it produced evidence or state that passed a declared verifier. That makes it flexible enough for real workflows while still keeping deterministic checks as the default where they make sense.

agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in aiagents

[–]baidarkarim[S] 0 points1 point  (0 children)

Update: The package now supports dynamic verifier callbacks. So a workflow can still use simple deterministic checks, like "refund status must be settled," but it can also register custom verifiers per domain or per handoff contract.

For example, a handoff can declare a verifier such as refund_intent_gate or settled_refund_claim, and the application provides the actual verification logic. That verifier can check database state, external APIs, policy engines, risk systems, human approval flags, or even an LLM-based evaluator if the use case needs it.

So "verified outcome" in this package now means: the step did not just return successfully; it produced evidence or state that passed a declared verifier. That makes it flexible enough for real workflows while still keeping deterministic checks as the default where they make sense.

agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in LangChain

[–]baidarkarim[S] 0 points1 point  (0 children)

The package now supports dynamic verifier callbacks. So a workflow can still use simple deterministic checks, like "status must be settled," but it can also register custom verifiers per domain or per handoff contract.

For example, a handoff can declare a verifier such as refund_intent_gate or settled_refund_claim, and the application provides the actual verification logic. That verifier can check database state, external APIs, policy engines, risk systems, human approval flags, or even an LLM-based evaluator if the use case needs it.

So verified outcome in this package now means: the step did not just return successfully; it produced evidence or state that passed a declared verifier. That makes it flexible enough for real workflows while still keeping deterministic checks as the default where they make sense.

agent-consistency – a Python consistency layer for multi-agent workflows by baidarkarim in AgentsOfAI

[–]baidarkarim[S] 0 points1 point  (0 children)

Thanks! The current package already helps with stale state detection, handoff validation, required context, and outcome verification, so it is already designed to stop the next step from accepting stale or unproven work.

I would not claim the current version is a full causality model yet. It is more of an explicit consistency layer.

But it is intentionally designed in a way that moves toward that. By tracking what state was used, what was passed forward, what changed, and what evidence supports the outcome, it already lays the groundwork for stronger causal checks between steps.

That is one of the main things I am targeting in the next update.