Moving beyond raw vector memory and linear step-by-step prompting toward Self-Evolving Procedural Graphs. Instead of relying on unstructured history, agents structure procedural knowledge into explicit graphs of (procedure, relation, procedure) triplets. When they fail, an offline "LLM Refiner" mutates the graph structure to encode new rules, prerequisites, and pitfalls. The benchmark results are impressive — but most procedural graph frameworks rely on deterministic, offline feedback loops (run rollouts, hit a validation gate, update between runs). In production, that breaks down in enterprise deployments:
1 · THE OFFLINE LATENCY PENALTYReal-world runtime environments don't wait for your offline optimization epoch. If an API contract changes or a third-party service throws a brand-new error code, an agent backed by a static procedural graph cannot dynamically repair its topology on the fly.
2 · NODE LOCALIZATION COLLAPSEMapping complex, noisy real-world feedback to discrete graph nodes assumes environment states map neatly to predefined schemas. When edge cases occur, the agent fails to identify its active node state — defaulting to unconstrained generation or repeating invalid steps.
3 · COMBINATORIAL GRAPH BLOATTo handle thousands of real-world edge cases deterministically, explicit graph topologies explode in size — leading to context-window congestion and retrieval latency.
REAL-WORLD FAILURE MODE · AUTOMATED LOGISTICS AGENT
Graph Path: [Verify Order] → [Check Warehouse API] → [Process Refund]
The incident: during a peak sale, a shipping API suffers micro-outages and throws an unmapped error code (ERR_SYNC_TIMEOUT_503). Because the state can't be localized to an existing node, the graph offers no guidance — the system defaults to an unmapped path (an unauthorized refund, or a crash), and can't create an adaptive [Retry with Backoff] node until an engineer runs an offline evolution pass.
Where multi-agent system design must go next:
- Dynamic Runtime Node Spawning — agents instantiate transient, conditional subgraphs during online execution
- Hybrid Latent–Explicit Memory — rigid topological graphs for core logic + continuous/latent state for chaotic edge cases
- Real-time Local Graph Repair — moving past expensive batch evolution to fast, localized runtime updates
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PhD — Multiscale Modelling of Aerospace Materials, Swansea University, UK
Patented AI/ML algorithm for Centrica PLC
Collaborator: NPL UK · Max Planck · Fraunhofer
Fortune 500 clients across 10+ industries
NATO Contractor — defence & security engagements
