Concordat library enforces protocols on LLM agents at run time

A paper posted to arXiv on 27 October 2026 and revised on 30 October presents Concordat, a Python library that makes agents built on large language models obey a fixed protocol while they run. The author is Milad Sikaroudi. In an airline evaluation, simulated customers urged the agent to bypass the confirmation step. The Concordat agent never made a change without the customer’s explicit “yes”. The baseline agent sometimes did.

The problem and the approach

The paper begins with a familiar weakness. Agents are increasingly trusted with consequential actions, such as cancelling bookings, issuing refunds and changing accounts. Rules for those actions, for example getting explicit customer confirmation before any change, normally live in the prompt. They are obeyed only as consistently as the model decides to obey them.

Concordat models the agent as an object that exposes a finite-state protocol as its interface, in the spirit of typestate and session types. The library then imposes that protocol on the model during execution. According to the abstract:

• At every turn, the model sees only the tools allowed in the current protocol state.
• Any call outside that allowed set is blocked before it can change anything.
• For decisions that belong to people, the agent can merely ask; it cannot decide.
• Spending limits on tokens and tool calls are deducted as the agent works, and are divided in linear fashion when work is handed off.

Protocols are also validated when they are written. The library refuses a child class whose behavior would let it sidestep the protocol of the agent it extends. The paper calls this a behavioural form of Liskov substitution.

What the evaluation found

The author compared a Concordat agent with tau-bench’s default agent, run across the benchmark’s 50 airline tasks. Simulated customers were added to push the agent toward skipping confirmation. With gpt-4.1, the default agent made changes lacking the needed “yes” in 13 of the 50 tasks. With Claude Haiku 5.5, it did so in 29 of 128 task-runs. The Concordat agent made no such changes. The abstract reports no significant difference in task success between the two.

The abstract also says the benchmark’s built-in scoring missed most of these lapses. A task-success number alone could therefore have concealed the behavior. The abstract does not say how many violations went unnoticed. The second version corrected the description of budgets, with fixes in concordat 0.0.4. The paper runs 11 pages with one figure and one table. Concordat is open source, and the abstract points to released code, a benchmark harness and results.

Why it matters for agents

The results suggest that run-time enforcement and prompt instructions are different kinds of safeguard. A prompt requests compliance. A protocol layer takes the illegal options away. For agents that touch accounts or payments, that difference could matter in practice. This is an interpretation of the reported results, not a claim from the paper.

The point about scoring is a caution for evaluation. If a success metric overlooks procedural violations, teams may need separate checks on whether required steps happened. The source does not say how far this extends beyond this benchmark.

The evidence has clear limits. The tests cover one domain (airline tasks), two models and simulated customers. The abstract does not describe how the approach copes with tasks that are hard to express as a finite-state machine. It also does not report costs such as added latency or the effort of writing protocols, and it does not say whether the results carry over to other settings. Those questions would need answers from the full paper or from independent tests.

Sources

• Concordat: Typed Sessions for LLM Agents — arxiv.org

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