Agentic AI could let archaeologists simulate the pasts that might have been

Archaeology has spent more than a century building competing stories about the human past, and it has never quite agreed on how to test them. Processualists, post-processualists, phenomenologists, neo-Marxists and cognitive archaeologists continue to interpret the same fragmentary remains in incompatible ways, and a new open-access paper in the journal AI & Society argues that artificial intelligence may finally offer a way out of this impasse. Mark Altaweel of University College London and Maurizio Forte of Duke University propose coupling agentic AI with agent-based modeling to create autonomous simulated societies that could turn interpretive debate into a form of computational experimentation.

The core problem the authors identify is structural. Archaeologists work from material remains that underdetermine the social behaviors that produced them, so theory tends to cluster into interpretive camps rather than converge on consensus. Even the influx of scientific methods, from ancient DNA to isotope analysis, has largely been used to support pre-existing positions rather than adjudicate between them. As the authors note, relativist perspectives have even suggested that plurality of interpretation should simply be accepted, since theory in archaeology may never be measurable in the way it is in other sciences. What they propose instead is to narrow the space of plausible explanations rather than to close it entirely.

Their starting point is a concept Forte has developed over the past decade and a half: the potential past. Rather than seeking a single factual reconstruction, the potential past reframes archaeological theory as a generator of constraints, premises and mechanisms that define what was possible. Digital reconstructions, in this view, are not replications of truth but speculative environments, and theory becomes a way of bounding the range of plausible conditions under which social, cognitive and material phenomena could have emerged. The approach shares affinities with counterfactual history, but extends it by focusing on perception, embodiment and meaning-making rather than only on events and causal sequences.

Technically, the proposal distinguishes four families of AI. Discriminative models classify existing data; generative models produce new outputs such as reconstructions of artifacts; agent-based modeling simulates autonomous entities interacting within an environment; and agentic AI refers to systems given goals that autonomously determine how to pursue them through planning, tool use, memory and adaptive decision-making with minimal human intervention. The authors observe that nearly all AI in archaeology to date, documented in a survey of more than 27,100 publications over a decade, has been discriminative or generative, and case-specific. They state that they know of no existing use of agentic AI in archaeology, which is precisely the gap their architecture targets.

The key innovation lies in where theoretical commitment is encoded. In classical agent-based modeling, researchers hand-code behavioral rules for their agents, drawing on ethnographic analogy or historical records. That practice is interpretable and explicit, but it risks importing researcher bias and excluding behaviors nobody thought to program, a serious limitation when the behaviors themselves are what is under investigation. In an agentic system, the modeler instead specifies goals, perceptual apparatus and environmental constraints, and the decision engine generates the action sequences. Theoretical constructs such as cooperation or ritual authority become goal specifications rather than hand-written rules, and variation across runs arises from the engine’s sensitivity to context rather than from random draws alone.

The authors are careful about what they are and are not claiming. Emergence, they stress, is a property of complex adaptive systems that a model may or may not exhibit, and it remains an empirical question whether a coupled ABM-agentic system produces meaningful emergent patterns. What agentic AI adds is a reconfiguration of the source of agent decision-making: the space of possible actions is no longer exhaustively specified in advance but generated dynamically in response to each agent’s perceptual and internal state. They even argue that the resulting interpretive indeterminacy, where the engine’s reasoning is inspectable but not fully transparent, is a feature rather than a defect, analogous to and in some respects replacing classical stochasticity.

For the decision engine itself, the authors favor small language models, domain-specific language models and hierarchical reasoning models over general-purpose large language models, whose inference costs and contemporary web-derived training data fit poorly with archaeology’s disparate and often non-digital evidence. Their proposed training pipeline has three stages: a general pre-trained base model from families such as Phi, Mistral or Gemma; domain adaptation on excavation reports, archaeological databases such as ARIADNE, tDAR and Open Context, and curated ethnographic records; and task-specific fine-tuning on annotated decision examples, which they identify as the largest methodological unknown. At each decision step, the engine receives a persistent system specification, a rolling context window describing the agent’s perceptual state, and a recent action history, returning a structured action in JSON.

On the simulation side, the authors survey the agent-based modeling ecosystem and conclude that Python-native platforms, particularly Mesa and Repast4Py, are the most immediately viable for coupling, since agentic frameworks such as LangChain, AutoGen and CrewAI are themselves Python-based. NetLogo remains valuable for conceptual modeling but harder to bridge to external AI engines, while GAMA excels where spatial and perceptual modeling is central and MASON suits tightly embedded high-performance applications. Three integration patterns are on offer: call-based coupling, local embedding of a small model in the same process, and batched coupling that trades granularity for throughput. For simulations of dozens to hundreds of agents over simulated decades, the authors judge call-based or batched coupling with a locally hosted small model the most practical starting point.

What might such systems actually simulate? The authors sketch applications ranging from chaîne opératoire analyses of craft traditions to logistics, governance and lived experience. In one illustrative scenario, agents tasked with mobilizing monument construction would generate their own tasks for obtaining materials, constrained by technology and climate, with outcomes ranging from conflict to cooperation across runs; consistency in specific outcomes across many runs would indicate what was likely. A worked example models the Paleolithic cave of Lascaux, where agents move through a torch-lit spatial layout under constraints on vision angle, torch range, innovation rate and copying bias, revealing how perceptual limits and social interaction shape the emergence and concentration of painted motifs. Cognition in these systems is not pre-scripted but enacted through continuous perception-action loops, with internal states such as attention, fatigue and familiarity biasing decisions.

The deeper stakes are epistemological. The authors argue that agentic simulation does not reduce interpretive uncertainty but makes its architecture explicit: theoretical commitments become visible in goals, perceptual inputs and constraints, and running the coupled model under varying specifications becomes the mechanism of analysis. Falsifiability shifts from verifying a single narrative to evaluating which theoretical configurations fail to produce coherent, plausible worlds, with empirical evidence serving as constraint and corrective rather than being replaced. The authors acknowledge open problems, including the cost of engine-mediated decisions at scale and the challenge of validating that generated behavior is plausible rather than merely fluent. But they position archaeology, with its deep engagement with fragmentary evidence and multivocal interpretation, as an ideal testing ground for AI that prioritizes emergence over optimization, and they suggest that simulated pasts that think back may ultimately challenge not just what humans did, but how we know it.

Subject of Research: Integrating agentic AI with agent-based modeling to test archaeological theory through simulated potential pasts

Article Title: Archeological theory and AI: the potential past through agentic systems

Article References: Altaweel, M., & Forte, M. (2026). Archeological theory and AI: the potential past through agentic systems. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03287-0

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03287-0

Keywords: agentic AI, agent-based modeling, archaeology, archaeological theory, simulation, potential past, small language models, hierarchical reasoning models, cyberarchaeology, emergence, Mesa, LLMs