In most cases, AI agents fail not because they lack intelligence, but because they lose critical details as soon as workflows get long or complicated.
Once upon a time, a compliance officer asked the company’s dedicated AI agent to review the entire website. After scanning thousands of pages and policy documents, the valiant agent confidently declared every page non-compliant with HIPAA. A surprising conclusion, given that this business unit doesn’t handle health data at all.
Or take the legal team that fed an agent a contract wrapped in hundreds of attachments, meeting transcripts, and redlines. The agent approved a clause in one step, flagged it in the a regulatory risk in the final report and argued fervently for keeping it when confronted about it.
But the least cheerful tale comes from a customer support lead who handed an AI agent a long-running ticket with logs and months of prior conversations. By message twelve, the agent confidently proposed a new series of fixes. Most of which the customer had completed weeks earlier.
Different industries, same moral to the story:
Once the context gets too long or too tangled, the agent can’t hold the thread.
But why?
The Architectural Constraint
It all boils down to the fact that AI agents don’t carry memory forward between steps. Each action begins with a blank slate. They only “know” what sits inside the current prompt, while context windows act like temporary scratchpads. Useful for a moment, wiped clean for the next.
Expanding these windows sounds like the easy fix, if it weren’t for the fact that transformer models impose quadratic cost scaling. In simpler terms, doubling the context can quadruple the compute cost and latency. This mathematical constraint makes very large windows economically unsustainable for real workloads.
Let’s pretend there’s a world where costs aren’t an issue. As counterintuitive as it sounds, even then, coherence wouldn’t be solved. Larger windows don’t create memory because the model doesn’t prioritize, structure, or retain information the way humans do. Every token is treated as equally important, so key constraints and earlier decisions get buried long before the window is full.
And the commonly discussed alternatives don’t fix that either:
- Vector databases store facts, not state; they’re filing cabinets, not decision logs.
- RAG (Retrieval-Augmented Generation) retrieves static information, not the evolving steps of a workflow. It can’t track what changed between step 3 and step 17.
- Fine-tuning bakes in historical patterns but cannot manage live decisions or mutable constraints.
None of these address the real source of drift: tool output noise. As agents call APIs, parse PDFs, or process logs, they accumulate enormous amounts of irrelevant text (verbose JSON, HTML, status messages) that bury earlier instructions long before the token limit is reached. So, the context doesn’t run out, it gets diluted.
The problem is magnified in multi-agent systems. Research shows coordination alone can consume 15× more tokens than single-agent work. Without shared memory or a governance layer, multi-agent systems fail 77.5% of the time. In other words, a task that costs $0.10 for one agent routinely becomes $1.50 when spread across several, and still produces inconsistent results.
Operational Collapse
Context loss can reduce quality and maybe some organizations could cope with that. Unfortunately, this structural quirk of current AIs can lead to the collapse of enterprise workflows.
A fraud-detection agent may correctly identify an early anomaly, then lose track of that insight as more transactions stream in, producing contradictory decisions with no audit trail.
A clinical triage agent can begin with the right contraindications in mind, then forget them as patient history accumulates, producing recommendations clinicians cannot safely use.
In multi-agent operations, one agent may update a policy or schema while another continues acting on the old version. Without a shared state, they drift apart silently.
These aren’t productivity slips. They are audit failures, compliance risks, and liability events waiting to happen.
The Security Boundary
Context drift also opens the door to security risks traditional controls cannot detect.
An agent processing a vendor PDF may ingest hidden instructions embedded in the document. Because everything it reads becomes part of its working context, the agent absorbs those instructions as legitimate. A single poisoned spreadsheet cell in a RAG pipeline can do the same, subtly rewriting the agent’s intent. This is called memory poisoning and it bypasses model-level guardrails entirely because:
- LLMs reason probabilistically, trying to be helpful
- Security demands deterministic rules, not suggestions
Once contaminated, a corrupted instruction can spread across systems and agents, because each inherits and rewrites an unstable working context. For enterprises already struggling with data silos and uneven governance, this multiplies risk.
What Production Systems Require
Production-grade agents need what distributed systems needed decades ago:
persistent memory outside the execution layer. In practice, this means:
- Storing workflow state in durable infrastructure (databases, state stores, control planes) not in a temporary context window;
- Using hybrid memory architectures that separate fleeting session details from long-term facts and policies;
- Enforcing identity-anchored permissions that shift dynamically with the agent’s mission;
- Coordinating multiple agents through deterministic control planes, ensuring everyone works from the same source of truth;
- Maintaining context provenance: what happened, why it happened, and what information drove each decision.
For regulated industries, audit trails, checkpointing, and reversible planning are baseline requirements.
Emerging standards like the Model Context Protocol (MCP) reflect the same direction: the industry is moving toward formal, external context layers, much like the web needed sessions to become useful.
The Coherence Layer
Solving data fragmentation gives agents the ability to finally see the information scattered across systems. But access alone doesn’t create reliable execution. Coherence is what turns visibility into real autonomy.
Achieving coherence requires systems thinking, not bigger models or longer prompts. Production-grade agents need an external foundation that stabilizes identity, memory, and policy across every step of a workflow, so decisions hold together even as tasks grow more complex.
This architectural layer is emerging as the key differentiator between demos that look impressive and systems that can be trusted at scale. It’s the layer that transforms AI agents from helpful assistants into governed, accountable participants in enterprise work.
At Civic, we’re working hard to incorporate systems thinking into Civic Nexus, so that we’re taking context management issues into account.
