Context Is the Product
Two service interactions, same model.
In the first, the agent sees the question and little else. It improvises. The answer sounds plausible. The customer escalates. A human reconstructs the account from three systems. Total cost doubles.
In the second, the agent sees the question, account status, applicable rules, prior contacts, and its own limits. It resolves or escalates with the file attached. Same model. Different architecture.
Executives do not need to read the protocol spec to grasp that difference. They need to demand it in production reviews.
What MCP Actually Organizes
Strip the acronyms away. MCP is a structured way to carry intent (what we are trying to do, under what constraints), connect to authoritative sources (not whatever API was fastest in sprint three), and leave evidence (what was retrieved, invoked, decided).
It sits between the agent runtime and the systems you already own. It does not replace your integration layer or data platform. It gives agents a governed on-ramp to both.
Questions Worth Asking Before You Fund Scale
What is the minimum context set before this agent may act? If you have not defined it, the agent will act on whatever is nearby.
When sources disagree, which wins? "The model will figure it out" is not a data strategy.
What is retained after the action — context and outcome together? Logs without context make post-incident review guesswork.
Who approves changes to what the agent is allowed to see, and who can stop it when quality drops?
Those four questions should be answerable from a governance packet, not from a engineer's laptop.
Failure Patterns I See Repeated
Context sprawl — feeding everything "just in case." Stale fields pollute good ones. Minimum viable context beats maximum possible context.
Session amnesia — constraints drop after a handoff between tools or sessions. If intent does not persist, you rebuilt the problem MCP was meant to reduce.
Authority ambiguity — two systems, one field, no owner. Agents harmonize conflicts unpredictably. Humans pay for it.
Evidence gaps — action logged, context not. You cannot explain the decision later, which is a problem in regulated environments and a lawsuit waiting in consumer businesses.
Regulated Environments
Insurance, banking, healthcare — anywhere a examiner asks "show your work" — context trails are not a nice-to-have. Explainability is reconstructing what was known. Reproducibility is proving it. Accountability is naming who approved the schema and the sources.
MCP is one way to implement that discipline. The discipline itself is non-negotiable.
A Practical Posture
Pick one bounded use case. Write the context contract: required fields, sources, retention, owner. Break it on purpose in test — stale data, missing source, conflict — and verify the agent escalates instead of guessing. Review quality monthly: completeness, freshness, authority compliance. Expand only when that packet is boringly reliable.
Key Takeaways
- Agents fail on context architecture more often than on model choice.
- MCP matters when it makes intent, sources, and evidence visible to governance — not when it is installed as a checkbox.
- Minimum context, explicit authority, combined audit trails, named owners — the rest is implementation detail.
Strategic Recommendations
- Require a context contract per agent before production.
- Publish source-authority maps per data domain; resolve conflicts in data governance, not at runtime.
- Test degraded-context scenarios as seriously as happy-path demos.
Next Steps
- Audit one live agent integration: does context persist, and can you replay a decision?
- Stand up a single reference implementation with full context discipline before scaling MCP footprint.
- Name a context owner with authority to pause agents when quality slips.
