Context Silos: When the System Knows But the Decision-Maker Doesn't

Consider an online commerce system at checkout.

The checkout agent evaluates an order. Device fingerprint matches recent activity. Shipping address is verified. Transaction amount is within normal range. Payment method has a clean history. Every signal the agent can see looks normal. The agent approves the transaction.

At the same moment, a behavior agent is processing clickstream data in the company's lakehouse. It detects a weak but meaningful pattern: the user arrived directly at the checkout URL without browsing. No product searches, no category navigation, no comparison shopping. Just a direct path to a high-value purchase.

By itself, this is common and non-definitive. Plenty of legitimate customers bookmark checkout pages or click through from email promotions. But the behavior agent recognizes this pattern as a known precursor in account-takeover scenarios—especially when combined with an otherwise normal-looking transaction. The attacker keeps everything within bounds. The only early warning is behavioral.

The behavior agent records this interpretation. It's computed correctly. It's computed on time. The checkout agent never sees it.

The laptop ships. Thirty-six hours later, the charge is disputed. The account was compromised.

Why It Wasn't Caught

The natural instinct is to look for what went wrong in the pipeline. Was data missing? Was the model undertrained? Was processing too slow?

None of these.

The behavioral data existed. The pattern was detected. The interpretation was computed within the decision window. Everything worked as designed.

The failure wasn't in the processing. The failure was in the architecture. The behavior agent's knowledge existed in a system the checkout agent couldn't consult. Not wouldn't—couldn't. The lakehouse wasn't part of the checkout agent's decision context.

The system knew. The decision-maker didn't.

The Pattern

This is a context silo: relevant context that exists but can't participate in a decision.

It's different from missing data. The data wasn't missing—it was there, in another system.

It's different from stale data. The insight wasn't stale—it was computed in real time.

It's different from a model failure. The model didn't fail—it correctly identified the risk.

The failure is structural. Knowledge was produced in one place. A decision was made in another. There was no shared context between them.

Why This Keeps Happening

Most enterprise architectures are organized by function, not by decision.

Operational systems handle transactions: fast writes, ACID guarantees, low latency. Analytical systems handle insights: complex queries, large scans, batch processing. This separation makes sense for the workloads each system is optimized for.

But decisions don't respect this boundary. A checkout decision needs both: operational state (is the payment valid?) and analytical insight (does this behavior look suspicious?). The architecture gives each agent access to its own slice. Neither sees the full picture.

Each system advances on its own schedule. The operational database updates immediately. The lakehouse refreshes on its own cycle. There's no shared moment of truth—no single representation of reality that both agents can query at decision time.

The architecture creates the silo.

Why the Obvious Fixes Don't Work

Faster pipelines don't help. Latency wasn't the problem. The behavior agent computed its insight in time. The problem was that the checkout agent had no way to see it.

More API calls don't help. The checkout agent didn't know to ask. It had no awareness that behavioral context might be relevant, no integration point to query it, no schema to interpret the response. You can't call what you don't know exists.

Caching doesn't help. You can't cache what you don't know you need. The checkout agent wasn't missing a slow response—it was missing an entire category of context.

Better models don't help. The behavior model worked. It detected the pattern. The problem wasn't detection. It was delivery.

The problem is structural. Faster, smarter, more connected versions of the current architecture still leave context siloed. The decision-maker still sees only what its system contains.

What's Required

For agents to operate constructively—for one agent's knowledge to inform another's decision—context can't be siloed by system.

This doesn't mean every agent needs access to every piece of data. It means that context relevant to a decision must be reachable when that decision is made. Not eventually. Not after reconciliation. At the moment.

This requires shared memory: a single context layer that both the checkout agent and the behavior agent can read from and write to. A place where the behavior agent's interpretation becomes visible to any agent whose decision it might inform.

The formal term for this requirement is Decision Coherence: interacting decisions must be evaluated against a coherent representation of reality at the moment they're made. The system architecture that provides this is called a Context Lake.

Context silos aren't a tooling problem. They're an architectural problem. Solving them requires rethinking where context lives—and what it means for agents to share it.