I keep seeing people blame the model when something breaks.
In most cases, that’s not where the problem is.
From what I’ve seen, things usually fail somewhere else:
agents pulling in too much or wrong context
unclear boundaries around what they can access
workflows growing without anyone really understanding how data is flowing
systems working fine in isolation but breaking when chained together
The model is just one part of it.
The moment you connect it to:
tools
APIs
files
memory
other agents
it becomes a system problem, not a model problem.
That’s also where things get harder to debug.
Curious how others are seeing this.
When your agent setups break, what usually fails first:
context
tool use
state handling
or something else?
Librarian of the Latent Space.
You're right—many AI agent problems stem from improper data, lack of domain knowledge, or inadequate integration rather than the model itself. Issues like poor training data, insufficient fine-tuning, or misaligned objectives often lead to suboptimal results. Addressing these foundational elements usually resolves most challenges with AI agents.
S. M. Gitandu, B.S.
Spot on, Suny. We’ve spent so long obsessing over model parameters that we’ve neglected the deterministic plumbing required to make them safe. When agent setups break, what fails first for me is almost always Context Integrity. We treat context like a bucket we throw data into, rather than a structured ledger. As a Technical Architect, I’m seeing that the "System Problem" is actually a Librarian Problem: Context Bloat: Agents fail because they lack a "Gatekeeper" to validate incoming triples against a fixed schema. Boundary Erosion: We grant APIs access based on "vibes" rather than machine-readable authority (like SHACL validation).