The buzz around context graphs and what it means for institutional operations
By Deepak Sheoran, Co-Founder and CTO, DwellFi
If you’ve been anywhere near agentic AI conversations lately, you’ve probably noticed “context graphs” popping up everywhere.
At first it sounds like yet another tech term we’ll overuse for a few months and then quietly retire. But the more you build and ship agents into real workflows, the more you realize: people are reaching for a new phrase because we’re hitting a real limitation in the stack.
When an agent is only answering questions, missing context is annoying.
When an agent is doing work—updating systems, sending client comms, reconciling numbers, drafting reports, triggering approvals—missing context becomes expensive. Sometimes risky. Always hard to debug.
That’s why context graphs are trending: they’re an attempt to make “what the organization knows” usable for agents in a way that is structured, permissioned, time-aware, and auditable.
And that’s exactly where DwellFi lives.
Why we needed a new “context” concept in the first place
Enterprises already have a lot of “knowledge”:
- systems of record (CRM, accounting, fund admin systems, ticketing tools)
- file drives and data rooms
- PDFs, spreadsheets, emails, exported reports
- tribal knowledge held by subject-matter experts (SMEs)
But agents don’t succeed just because you gave them more text. They succeed when you give them the right working set for the moment: the important facts, the relevant relationships, the policies that apply, and the history of how similar situations were handled.
In other words: agents need context that behaves less like a document dump and more like an operational map.
That’s what people mean by a “context graph.”
The ontology debate: it’s not “either/or”
A lot of the online discussion has become a debate about ontologies—should we define the world upfront (prescriptive), or let structure emerge from usage (learned)?
In practice, most teams end up doing a blend, whether they admit it or not.
Here’s the pragmatic view:
- Some structure is already widely agreed upon (people, accounts, funds, transactions, documents). Reinventing those definitions from scratch is usually wasted effort.
- The value is in the parts that aren’t neatly captured by your systems today: cross-system relationships, exceptions, approvals, and the “why” behind actions.
So rather than obsessing over “prescribed vs learned,” a better question is:
What foundations can we reuse, and where do we need to capture new context that only shows up during execution?
That second category is where context graphs earn their keep.
The part that actually hurts: time and “what did we know then?”
One of the most underrated challenges in enterprise AI is temporal accuracy. Most systems can answer: “What’s the current state?”
Fewer can answer:
- What was true when we made that decision last month?
- Which version of the document did we rely on?
- What did the agent see before it chose a path?
- When did this fact become valid, and when did it stop being valid?
This sounds philosophical until you try to audit an automated workflow.
If an agent pulls a figure from a document, and the document gets replaced later, you need to know what happened without guessing or arguing. The ability to “time travel” through context is the difference between:
- trustworthy automation and
- mysterious automation
A modern context system needs a built-in sense of an “event clock” — a way to anchor claims and actions to time.
“Decision traces” vs “reification”: call it what you want, but capture the trail
Another lively thread in the discourse is what to call the record of how actions happened. Some people like the term “decision traces” because it’s intuitive: the system should remember why an exception was granted, what precedent was used, and who approved it.
Others dislike the framing because computers don’t “decide” like humans. They prefer a more precise concept: represent statements about statements — attach provenance, conditions, and evidence to a claim in a structured way (often referred to as reification in graph circles).
If you’re building for institutions, the naming matters less than the outcome:
When an agent produces an output or takes an action, you want a durable record of:
- the inputs it used (and where they came from)
- the policies or constraints that applied
- any exception path that was triggered
- the approval chain (if humans were involved)
- the final write-back and downstream impact
That trail is how you debug. It’s how you govern. It’s how you build confidence with operators, auditors, and clients.
What practitioners learn fast: context is relationships, not just retrieval
One of the most consistent “lessons learned” you see from teams deploying agents: the problem isn’t that you can’t retrieve a relevant paragraph.
The problem is that real work depends on relationships:
- which entity owns what
- what is linked to what
- which policy applies to which case
- what changed since the last run
- which exceptions are allowed for which category
This is why graph-shaped context is attractive. It’s not because graphs are fashionable; it’s because organizational reality is relational.
The moment you try to automate something like reconciliation, reporting, or client service, you’re traversing a web of:
documents → entities → definitions → approvals → outcomes.
The leadership piece: intellectual honesty is a feature, not a vibe
There’s also a non-technical theme that shows up in this trend: leadership responsibility.
As agents become more capable, it’s tempting to demand confidence and speed. But in high-stakes operations, the best systems don’t just act fast—they are honest about what they know.
That means designing for behaviors like:
- “I can’t find that in the approved sources.”
- “Here are the documents and fields I used.”
- “This value conflicts with another source; here’s how I resolved it.”
- “This requires approval; routing to the right person.”
In other words, the future of AI in operations isn’t just automation. It’s accountable automation.
How DwellFi turns this into something teams can actually use
At DwellFi, we think about context graphs in a very practical way: how do we help institutional teams convert messy operational reality into agent-ready context — and then turn that context into execution?
Here’s how our platform maps to the needs behind the trend:
- Capture institutional context where it actually lives
DwellFi’s Knowledge Library ingests and organizes information across 70+ document types and 250+ integrations, with role-based access control so the right context is available to the right people (and agents).
- Structure the messy parts (without forcing teams to become data engineers)
A lot of “context” is trapped in unstructured docs—PDFs, statements, capital calls, reports, scanned forms.
With AI Tables, teams can extract structured fields at scale, and keep source references so each value is traceable. That’s not just convenience; it’s the groundwork for auditable context.
- Put agents in the execution path (where context gets created)
DwellFi includes an agentic automation layer—RPA agents plus an agent builder—so workflows can run end-to-end with the right approvals and checkpoints.
This matters because context doesn’t get captured “after the fact” very well. It gets captured best while work is happening.
- Keep it enterprise-grade: security + model flexibility
DwellFi is LLM-agnostic and built for enterprise deployments. Customers can control which models are used, and keep data governance tight—essential for financial services and other regulated environments.
Context graphs are a response to reality, not a fad
The reason this topic is taking off is simple: agentic AI is forcing a new standard of rigor.
Teams are realizing that the differentiator won’t be who has the flashiest agent demo. It’ll be who can deliver:
- reliable context
- structured provenance
- time-aware truth
- governed execution
- and workflows that improve with use
That’s the direction DwellFi has been building toward: institutional context that can actually run operations.
If you’re exploring context graphs and wondering where to start, a good first step is to pick one workflow that’s document-heavy and exception-heavy, then build the context trail as you automate. That’s where the learning (and the ROI) shows up fastest.