Agentic Entity Resolution

By: Jeff Jonas  |  AI Assisted  |  100% Human Verified Feb 2026

The executive summary laid out the challenge: identity data is fragmented, previous solutions have fallen short, and legacy ER can’t keep pace with agentic AI. So what does a solution look like?

Agentic entity resolution changes this entirely. It’s what happens when an AI agent can autonomously prepare data, load and resolve entities in real time or batch, and then allow the user to conversationally explore and explain the results — all without requiring experts to configure, train, or fine-tune the system for each and every data source.

The Identity Intelligence Infrastructure and Its Interaction Modes

At the foundation is identity intelligence powered by entity resolution — the architectural capabilities it must possess are the focus of this article. On top of that infrastructure, four interaction modes define how agents, systems, and users engage with identity intelligence:

Agents Prepare Data — AI agents autonomously profile, map, and load data from any source and format into the ER engine, building and enriching the organization’s identity intelligence while eliminating the manual data engineering that has historically bottlenecked entity resolution projects.

Agents Converse — Agentic workflows query the ER engine programmatically, coordinate across multi-agent architectures, and orchestrate complex identity-dependent tasks — e.g., fraud triage, investigative analysis, and risk scoring — without human intervention. Identity intelligence serves as shared context across multi-agent systems, ensuring every agent operates from the same accurate, current view of who is who.

Pub/Sub Functions — Event-driven integrations enable real-time data flows into and out of the ER engine via streaming platforms, webhooks, and message queues — ensuring identity intelligence stays current as source systems change.

Users Explore — Analysts, investigators, and decision-makers conversationally explore the organization’s identity intelligence, interrogate matches, surface exceptions, and provide feedback that improves resolution quality over time.

Every interaction in an agentic ER workflow is agile, instant, and explainable. This is not a theoretical future. It’s happening now.

The convergence of agentic AI and entity resolution isn’t accidental; it’s inevitable. As organizations deploy AI agents to automate increasingly complex workflows involving identity, fraud detection, risk, and compliance, they need entity resolution that can keep pace with the autonomy, speed, and agility that agents demand.

The business impact of agentic entity resolution is compelling across multiple use cases:

Organizations need identity intelligence in their infrastructure that can be summoned instantly, process data in any format without configuration, and deliver accurate, explainable results in real time.

Nothing else will do.

Not every entity resolution system is fit for agentic use. To serve autonomous AI workflows, an entity resolution engine must possess six architectural capabilities that work together to deliver instant, agile, and accurate identity intelligence. These aren’t nice-to-haves — they’re non-negotiable requirements for the agentic era.

When an AI agent needs to know who is who and who is related to whom, it can’t wait minutes for an engine to spin up, initialize, and become ready to work. An agentic-ready entity resolution engine must boot and become operational in sub-second time — from cold start to ready-to-process in less than one second. No initialization period. No infrastructure to orchestrate before the service can begin servicing queries or resolving entities.

Most ER systems require minutes of startup time as they load models, initialize clusters, and prepare their runtime environment. In the agentic world, this is a non-starter. An agent needs to summon ER capabilities on demand, use them, and move on — treating entity resolution like an intelligent function to be called, not a monolithic platform to be managed.

The ideal form factor is a small, composable library — not a monstrous tech stack of sprawling clusters, dependencies, and infrastructure. It should work in memory for small, non-persistent datasets, and in conjunction with a backend data store for the persistence needed to deliver enterprise-scale identity intelligence. It must support serverless architectures, allowing agents to spin up ER capabilities on-demand without managing infrastructure, whether on AWS Lambda, Azure Functions, Google Cloud Functions, or containerized environments.

The agent doesn’t wait for infrastructure to provision or services to initialize. It calls the ER engine, and the engine is instantly ready.

This is also where most entity resolution systems fail the agentic test. Traditional systems require painstaking configuration for every new data source: data profiling, field mapping exercises, match-rule fine-tuning, model retraining, and often weeks of expert labor before the system produces accurate results. That dependency on human-in-the-loop configuration is a non-starter for agentic AI.

For agentic workflows, accuracy without training or fine-tuning unlocks something powerful. Data sources in entirely different formats — Parquet, Kafka, JSON or CSV files, a database, etc. — can be agentically prepared and combined with an organization’s existing identity intelligence in unprecedented time, no expert intervention required.

Agents prepare the data, summon the entity resolution service, and voilà — you know who is who. This is the kind of simplicity that agentic architectures demand.

Agentic AI doesn’t just need to know who individuals are in isolation — it needs to understand the relationships and hierarchies between them. This entity resolved knowledge graph (ERKG) provides far richer context for downstream RAG and ML than traditional entity resolution that lacks relationship awareness.

Resolved entities exist within a rich network of relationships: family members sharing addresses, beneficial owners linked to corporate structures, suspects associated with known bad actors. Ideally, these relationships are formed as records are resolved — so that this added context is available in the moment to serve the real time needs of agentic workflows.

Support for disclosed and discovered relationships means agentic workflows can leverage both known structural relationships and discovered hidden connections simultaneously, providing the most complete picture of how entities relate to one another.

Relationships transform entity resolution from a data quality exercise into an intelligence capability. An agentic workflow querying the engine doesn’t just get back “John Smith at 123 Main Street.” It gets John Smith connected to ABC Corp as an officer, linked to Jane Smith at the same phone, and linked to an address that appears on a fraudster list.

For organizations investing in context engineering — the discipline of optimizing the information provided to AI models at inference time — an entity resolved knowledge graph is among the highest-value context sources available. Rather than feeding agents raw, duplicated, and contradictory records, an ERKG provides clean, resolved entities with rich relationship context, dramatically improving the accuracy and reliability of downstream AI decisions.

For agentic use cases involving fraud detection, compliance screening, investigative analysis, risk assessment, ultimate beneficial ownership, householding, and marketing — relationship awareness delivers essential context for maximizing the value of autonomous workflows. The agent doesn’t need to call one system for entity matching and another for relationships. Agentic ER engines should deliver both: clean entities and the contextual networks they inhabit, ready for the agent to query, explore, and act upon.

Here is a truth about identity intelligence that nearly every organization learns the hard way: you will end up with aspirations for more data than you originally conceived.

It starts modestly. An organization decides to resolve 10 million customer records — enough to clean up the CRM and catch obvious duplicates. A few months later, the fraud team realizes that effective triage of new customer applications requires cross-referencing against watchlists, corporate registries, vendor databases, and third-party risk feeds. Suddenly the need is 100 million records. Then the board mandates a consolidated view across all business divisions — not just retail banking, but commercial lending, credit cards, and wealth management. Before you know it, you have a billion records. And still it’s not done, because the compliance team needs to incorporate sanctions lists, adverse media feeds, corporate hierarchies with officers, directors, and beneficial owners.

This isn’t a hypothetical. We’ve seen organizations begin with a 1.5 billion record deployment, only to discover the strategic necessity of combining data from additional divisions and external sources to ensure top-notch fraud detection and customer care. The coverage didn’t just grow — it multiplied. And in the agentic era, this trajectory only accelerates.

The nightmare scenario is investing in an entity resolution engine that works beautifully at 10 million records — and then hitting a wall. The engine slows to a crawl, resolution quality degrades, or the architecture simply can’t scale beyond its original design point. Now you’re trapped: years of integration work, accumulated knowledge, operational processes, and institutional trust are locked into a system that isn’t fit for purpose to grow into the initiatives that might get prioritized by the board and executive leadership.

An agentic-ready ER engine must scale without architectural ceilings. From thousands of records resolved dynamically in memory, to hundreds of millions on commodity hardware, to billions across distributed infrastructure — the engine must maintain the same resolution quality, the same sub-second response times, and operational simplicity that scales gracefully — even as infrastructure grows more distributed. Scaling up should mean adding resources, not re-architecting the solution. Scaling down should be equally natural — ideally the same engine that handles enterprise-scale workloads can also run on a laptop for development and testing.

The organizations that win in the agentic era will be those whose identity intelligence grows with them — absorbing new data sources, new divisions, new missions, and new regulatory mandates without ever hitting a wall. Choose an ER engine that can’t keep pace with your ambitions, and you’ll spend years and millions replacing it. Choose one that scales without ceilings, and you’ll never have to make that choice again.

There is a critical distinction between real time querying and real time resolution. Many entity resolution systems claim real time capabilities, but what they actually offer is real time querying of batch-based data that has already been loaded and resolved. That’s not the same thing.

When agents operate on stale identity data — yesterday’s entity graph, last week’s watchlist matches — they make decisions based on a view of the world that no longer exists. The consequences range from missed fraud to problematic approvals to compliance failures.

An agentic-ready ER engine must deliver true real time resolution — the ability to ingest a new record, resolve it against the full entity population, update all affected entities and relationships, and return results in milliseconds. Not after a batch cycle. Not after a scheduled refresh. Immediately — as the data arrives.

For agentic workflows, this distinction is existential. An applicant is denied, turns around immediately, and reapplies with slight variations — and gets in after enough attempts because the system can’t see the real time turnstile in motion. An agent monitoring transactions for fraud can’t tolerate a 24-hour delay between when suspicious activity occurs and when it’s detectable. An agent performing continuous vetting can’t rely on yesterday’s view of the world.

True real time means the engine can simultaneously ingest and resolve streaming data while also processing large batch workloads — without one mode degrading the other. This eliminates a fundamental architectural tradeoff that organizations otherwise face — choosing between separate batch and streaming pipelines, or accepting the complexity of maintaining both. It means sub-200-millisecond resolution times at scale. It also means that operational maintenance — deleting records to enforce retention policies, ingesting new batch reference datasets, updating watchlists — happens while the system continues serving queries and resolving entities without interruption. And it means that every query, every search, every resolution decision reflects the most current state of the data — accurate to the second, every second.

Batch-oriented systems that claim real time capabilities force organizations into uncomfortable choices: accept stale results, accept downtime for reloads, or maintain parallel A/B infrastructures to keep operations running while batches are processed. An agentic-ready engine eliminates this choice entirely. Resolution happens at the speed the data arrives, and the system is always current, always available, and always accurate.

Agentic workflows don’t have a single operating mode — and neither should entity resolution. Some tasks require resolving a small batch of records just-in-time, on-the-fly, with no persistence needed. Others require querying or updating a massive, long-lived entity resolved knowledge graph that serves as an organization’s canonical identity intelligence. An agentic-ready ER engine should support both.

Dynamic ER is entity resolution on demand — ephemeral, in-memory, and disposable. An agent spins up the ER engine, loads thousands of records entirely into memory, resolves them, extracts the results, and the instance disappears. No database server to install, configure, or maintain. No persistent infrastructure. Just instant, just-in-time identity intelligence.

This mode is ideal for agentic workflows that need to answer a bounded question quickly: resolve an investigative link chart to collapse the graph while revealing previously hidden connections — accelerating investigations, resolve a batch of records against each other before submitting them for ML fraud training, or cross-match a set of transaction counterparties to detect collusion. The agent treats entity resolution as a function call — invoke it, get the answer, move on.

Dynamic ER thrives in serverless and containerized architectures. An agent running on serverless architectures like AWS Lambda, Azure Functions, or Google Cloud Functions, or in a short-lived container can summon ER capabilities, process a workload entirely in memory, and shut down — with zero infrastructure footprint before or after. Dynamic ER also makes development, testing, and proof-of-concept work straightforward — no infrastructure to provision before getting started.

Persistent ER is entity resolution backed by a database — continuously maintained and always available. This is the mode that builds and sustains an organization’s identity intelligence infrastructure: an active, self-correcting entity resolved knowledge graph across every data source the organization has loaded over time.

When an agentic workflow needs to screen a new customer against the full population of resolved entities, or trace the complete relationship network around a subject of interest, or determine whether a newly surfaced record connects to any known bad actor across the enterprise — this is answered by the entity resolved graph. This is the organization’s identity index — delivering instant recall about who is who and who is related to whom, accumulated over months and years of continuous operation.

Persistent ER supports the highest-scale deployments — processing real time streams and large batch workloads simultaneously, servicing fraud detection, compliance screening, investigative analysis, and customer 360 programs that demand always-on, always-current identity intelligence.

The power of hybrid ER is that the same engine, the same resolution logic, and the same data format serve both modes. An agent doesn’t need to learn two systems or reconcile two sets of results. It uses dynamic ER for bounded, just-in-time workloads and persistent ER for enterprise-scale recall — and the resolution quality is identical in both cases.

This flexibility is essential for agentic architectures. An agent might extract records from the persistent ERKG, exclude sensitive records — say, classified holdings from the corporate security department — and then use dynamic ER to resolve the remaining records. The result is a clean view of what’s knowable after the exclusion of selected data, without ever modifying the persistent store. The two modes complement each other — dynamic for speed and agility, persistent for depth and recall.

Loading data and resolving entities is only half the story. The other half — and arguably the more transformative half for end users — is the ability to explore and interrogate entity resolution results conversationally, through natural language.

In the agentic era, users should be able to engage in a fluid, conversational dialogue with their entity resolution results:

This conversational layer transforms entity resolution from a technical, back-office operation into something accessible to analysts, investigators, compliance officers, and decision-makers. Anyone who needs to trust — but verify — their identity intelligence can do so conversationally, without having to navigate a new system or seek help from the technical staff.

Once identity intelligence is available in your infrastructure, entity resolution services can be summoned agentically — transforming operations across banking, insurance, and government:

These use cases are familiar to every organization dealing with identity data. What’s changing is how they’re executed. As the world embraces agentic workflows, the evolution from manual, batch-driven processes to autonomous, real time identity intelligence is the conversation every enterprise needs to be having.

When AI agents operate autonomously — preparing data, loading and resolving entities — organizations face critical governance imperatives: Who controls where sensitive data lives? Who has the right to see what? And how can every decision be traced and defended?

An agentic-ready entity resolution engine must provide fundamental governance advantages. Here are some of the most important requirements:

The ER engine should be able to run where your data is. But that’s only half the requirement. It must also run where your data needs to be tomorrow.

An organization’s identity intelligence is a strategic asset — arguably among the most sensitive and valuable data an enterprise possesses. Where that asset lives will change: cloud provider to cloud provider, cloud back to on-premises after an unacceptable outage or security incident or simply on the basis of cost, one jurisdiction to another as industry regulations change or tax policies shift. A merger demands consolidation into a new environment. A board directive mandates repatriation from the cloud to a sovereign data center. If your entity resolution vendor controls where the data lives, you don’t have control of your highly strategic identity intelligence.

An agentic-ready ER engine should come with complete portability — deployable on-premises, in any private or public cloud, within sovereign data centers, or across hybrid environments. An organization may choose to have a third party host or manage its identity intelligence — and that’s a perfectly valid decision. But it must remain exactly that: a choice, reversible at any time.

In the agentic era, where autonomous processes are leveraging identity data for real time decision making, this freedom of action isn’t a convenience. It’s an imperative. Without portability, your most strategic data asset could become a strategic vulnerability.

Agentic processes moving identity data across systems and workflows need complete visibility into where every piece of data originated. Without that provenance, governance becomes guesswork — you can’t enforce privacy regulations, you can’t determine what data can be shared with which process or system, and you can’t defend any action taken on the basis of that data. Many entity resolution systems lose this thread. Merge/purge processes discard data, survivorship rules pick winners and losers, and the link between a resolved entity and its original source records is severed.

Consider just one example of the implications. The Universal Declaration of Human Rights prohibits arbitrary actions against individuals — from detention to privacy interference to deprivation of property. When identity data lacks attribution, any action taken on that data risks being exactly that: arbitrary, because it lacks a verifiable factual foundation. Full data provenance isn’t just good practice. It’s a human rights imperative.

Whether a General Data Protection Regulation (GDPR), or other privacy law, right-to-be-forgotten request arrives or someone is removed from a watchlist, the entity resolution engine should be able to locate and remove the specific record and undo its ripple effects across related entities, in real time, without waiting for a periodic reload or refresh. Full attribution makes this possible: when every record knows exactly where it came from, surgical deletion becomes straightforward.

Every resolution decision should be traceable to source records, fully explained, and defensible. Regulators, compliance auditors, and legal teams will demand to know why specific matches were made, why others weren’t, and how entities evolved over time. An ER engine that can’t answer those questions — in detail, on demand — creates unacceptable risk for any organization subject to audit or regulatory oversight. And as organizations deploy enterprise-wide AI governance platforms to manage AI risks, compliance, and performance, entity resolution explainability becomes a critical input — providing the auditable, traceable identity decisions that governance frameworks require.

These governance capabilities aren’t features to be bolted on for compliance — they’re foundational architectural choices that should be baked in from day one. In the agentic era, where autonomous processes are making decisions using identity data, black-box resolution is indefensible — both operationally and legally when regulations require not just data transparency but decisional transparency.

The capabilities described in this article aren’t a wish list — they’re a checklist. The Senzing^® solution is a massively scalable, real time AI for entity resolution, engineered from the ground up with exactly these capabilities. Its core architectural choices are the product of decades of sustained engineering — refined through real-world deployments across government agencies, financial institutions, and global enterprises where getting identity wrong isn’t an option.

Delivered as a composable library — not a stack — that runs where your data lives, Senzing is the first identity intelligence solution for your infrastructure perfectly suited to serve both agentic and traditional workloads.