AI Agents and Smart Contracts Could Secure and Speed Last-Mile Medicine Delivery

A team of researchers at Hassan II University of Casablanca has combined blockchain smart contracts with large language model agents to orchestrate crowdsourced pharmaceutical last-mile delivery, reporting that in matched simulations the AI-assisted workflow cut route time by 25 percent and planning time by nearly 79 percent compared with a human-operated baseline. The work addresses a stubborn gap in pharmaceutical logistics: while blockchain systems excel at preserving a tamper-evident record of custody, they do little to help dispatchers forecast warehouse readiness, build routes, or choose suitable carriers in real time.

Pharmaceutical delivery differs from ordinary parcel logistics in critical ways. A late medicine can interrupt treatment, a temperature excursion can ruin the product, and a substituted or falsely confirmed delivery can harm a patient. Between a distributor and the final pharmacy or patient, traffic, varied vehicles, returns, and fluctuating demand make these obligations harder to satisfy. The Moroccan and wider North African context adds further complications, since small pharmacies and independent carriers may have uneven digital capacity, and any production system must comply with pharmaceutical, personal-data, and electronic-trust regulations.

The researchers’ answer is a ledger-grounded agent architecture in which the blockchain, not the language model, remains the authoritative source of operational truth. Smart contracts govern identity status, order assignments, capacity bookings, delivery evidence, and returns, while a real-time index and a Neo4j graph database organize contract events into retrievable relationships. An AI agent can only act on an order, carrier, booking, or delivery status if that record exists in verified platform data, substantially reducing the risk of decisions built on fabricated or unsupported information.

Four specialized agents divide the work. A Main Agent monitors the operation, retrieves similar past scenarios from graph memory, and coordinates specialist workflows. A Forecasting Agent estimates order arrivals, warehouse readiness, dispatch needs, and the risk of missing wave cutoffs. A vehicle-routing agent constructs feasible route plans and evaluates urgent insertions, while a Dispatch Agent carries out carrier checks, assignments, bids, bookings, and approved state changes in sequence. Numerical services compute forecasts and routes using portfolios of statistical and deep-learning forecasting methods and optimization solvers including ant colony optimization, particle swarm optimization, genetic algorithms, simulated annealing, proximal policy optimization, and deep Q-learning.

The routing logic applies lexicographic priorities rather than blending metrics through arbitrary weights: unassigned urgent orders come first, then unassigned standard orders, then minutes of lateness, and finally distance as a tiebreaker. Feasibility conditions enforce capacity limits, vehicle and temperature compatibility, valid carrier identity and eligibility, booking consistency, and locked route prefixes. Motorbikes carry either cold-chain or ambient products within a route but never both, while suitably equipped cars can mix both classes when packaging and capacity constraints permit. Urgent orders are prepared within 30 to 45 minutes and dispatched immediately after staging, while standard orders consolidate into waves at 10:00, 15:00, and 17:00.

Crowdsourced carriers introduce a trust problem that the framework tackles through verifiable credentials. Enrollment requires an authorized issuer to check official identity documents, driving licences, photographs with liveness checks, vehicle registration, insurance, and any cold-chain qualification. The approved carrier signs a one-time challenge to prove control of a wallet key, and the issuer issues a short-lived W3C verifiable credential binding the carrier, wallet, and capabilities. The blockchain stores only the credential fingerprint, issuer, validity period, and revocation status; personal documents and photographs remain encrypted off-chain. Pickup, delivery, and return events then link product scans, carrier signatures, time and location evidence, and pharmacy confirmations to the same digital identity.

Evaluation used five fixed-seed simulated operating days totaling 1600 orders, 40 carriers per day, 288 urgent orders, and 448 cold-chain orders, with each day replayed once through the human-operated baseline and once through the agent-optimized flow under identical conditions. The agent flow reduced route time by 25.0 percent and planning time by 78.6 percent, with consistent improvements in distance, urgent-order handling, assignment readiness, and proof completeness. A composite efficiency index reached 126.4 against the baseline’s 100, and the favorable direction remained stable across sensitivity analyses of the weighting scheme. Smart-contract functions were benchmarked separately on a local Ethereum virtual machine and the Sepolia testnet, quantifying gas, fees, and confirmation times for registration, order creation, booking, bidding, pickup, proof, and completion.

The authors caution that the evidence comes from simulation, not production. Five day-level replications with one distribution center and synthetic pharmacy profiles cannot capture real traffic, carrier misconduct, connectivity loss, sensor behavior, or user interaction, and the study did not conduct a dedicated hallucination test. Cryptographic credentials cannot guarantee document authenticity, prevent credential lending, stop a stolen key before revocation, or certify that the approved person physically performed every delivery. The next phase will involve the collaborating Moroccan distributor, first replaying live ERP and warehouse events in shadow mode, then running a monitored single-center pilot with vetted carriers and pharmacies, and finally scaling load across centers, carriers, and transactions.

For deployment, the researchers propose a phased production architecture that keeps personal data off-chain, uses a permissioned ledger among authorized partners, isolates external model providers behind an API privacy gateway, and routes agent recommendations through a human approval boundary before any smart-contract commitment. Compliance reviews must cover Moroccan pharmaceutical law, personal-data protection under Law 09-08, and electronic trust services under Law 43-20, while WHO good distribution practices make calibrated sensors, excursion alerts, and documented corrective actions operational requirements rather than optional features.

Beyond the specific Moroccan case, the study offers a template for adding operational intelligence to blockchain-based traceability without sacrificing accountability: the ledger supplies durable facts, deterministic tools handle computation, the language model coordinates context and sequencing, and every consequential action passes through explicit rules, auditable evidence, and human oversight. As agentic AI systems move into safety-sensitive logistics, the authors argue that reliability must be measured, not assumed, and future work will include dedicated hallucination tests, model comparisons, and quantitative security evaluation of contracts, carriers, identity, and agent behavior.

The distinction between traceability and orchestration helps explain why earlier blockchain pharmaceutical systems, however robust their records, left dispatchers largely on their own. Prior platforms built on Ethereum and Hyperledger demonstrated that role-based contracts, distributed file storage, and IoT sensing could make missing or inconsistent custody events easier to detect, and recent systems have combined ledger provenance with counterfeit-detection classifiers and certificate validation. Yet none of these contributions centered on real-time dispatch under fluctuating crowdsourced capacity, which is precisely where the Casablanca team positions its work.

The choice of graph-based retrieval rather than conventional text retrieval reflects a deliberate design judgment. In a retrieval-augmented setup built on a property graph, an order can be linked to its products, required temperature class, booking, carrier, route, and delivery proofs as explicit relationships, so the agent retrieves structured context rather than isolated passages of text. This matters in logistics, where the meaning of a record depends on its position in a web of custody events, and it reduces the chance that an agent reasons from fragments disconnected from the operational state.

The architecture also acknowledges that tool interfaces themselves create an attack surface. Malicious or misleading tool schemas, compromised servers, and excessive permissions can all corrupt an agent’s behavior, which is why the framework relies on allow-lists, authentication, input validation, and trace logging for every external call. This defensive posture extends to the model’s own tendencies: recent evaluations of language-model factual recall penalize confident wrong answers and reward appropriate abstention, a principle the authors carry into their design by ensuring the agent can abstain or escalate rather than guess when verified data is absent.

The evaluation methodology deserves attention in light of the broader research landscape. Reviews of generative AI in logistics consistently find that empirical field evidence remains scarce and that simulation dominates the literature, a pattern this study follows knowingly. By replaying five fixed-seed days through both workflows under identical demand, carrier pools, and order mixes, the researchers created a controlled comparison in which differences in route time, planning time, and proof completeness can be attributed to the coordination method rather than to environmental noise. The separate benchmarking of contract functions on a local virtual machine and a public testnet similarly isolates the cost of on-chain accountability from the benefits of agent coordination.

The lexicographic routing priorities also connect to a long-standing debate in optimization practice. Weighted objective functions force planners to express trade-offs in commensurable units, and small weight changes can silently reorder priorities in ways operators cannot anticipate or audit. By ranking urgent assignments, standard assignments, lateness, and distance in strict order, the framework makes every routing decision explainable as a sequence of dominance checks, which aligns naturally with the accountability requirements of pharmaceutical custody and with the need for authorized staff to understand and contest agent recommendations.

The identity model likewise reflects emerging standards beyond the pharmaceutical domain. Binding a short-lived verifiable credential to a proven wallet key, storing only fingerprints and revocation status on-chain, and keeping documents encrypted off-chain follows the privacy principle that blockchain should attest to facts without becoming a repository of personal data. Expiry and revocation mechanisms acknowledge that carrier eligibility is a continuing state, not a one-time gate, addressing risks such as credential lending or stolen keys only partially, as the authors themselves note.

Ultimately, the study’s contribution lies less in any single technique than in the division of labor it enforces: durable facts from the ledger, deterministic computation from numerical services, contextual coordination from the language model, and consequential state changes gated by contracts and human approval. Whether that division survives contact with live traffic, real carriers, and regulatory inspection is the question the planned shadow-mode replay and monitored pilot are designed to answer.

Subject of Research: Integration of smart contracts and AI agents for secure, traceable crowdsourced last-mile pharmaceutical delivery

Article Title: Smart contracts and AI agents for secure last-mile pharmaceutical delivery through crowdsourcing

Article References: Nadime, K. L., Haidar, D., Benabbou, R., & Benhra, J. (2026). Smart contracts and AI agents for secure last-mile pharmaceutical delivery through crowdsourcing. Discover Artificial Intelligence, 6(1), Article 1128. https://doi.org/10.1007/s44163-026-02206-y

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02206-y

Keywords: blockchain, smart contracts, AI agents, last-mile delivery, pharmaceutical logistics, crowdsourcing, large language models, vehicle routing, cold chain, traceability, verifiable credentials, supply chain