Context Poisoning & Memory Drift: How Production Agent Harnesses Prevent Hallucination Cascades in Long-Running Workflows

The 128k Token Illusion

Frontier foundation models now advertise massive context windows—ranging from 128,000 to over 1,000,000 tokens. To non-engineering executives, this sounds like permission to throw entire codebases, multi-year customer histories, and endless tool logs into a single prompt session.

In production multi-turn agent systems, this naive assumption causes catastrophic failures:

As an autonomous agent executes across 15 to 30 sequential turns, accumulated terminal logs, raw JSON payloads, and speculative reasoning traces systematically degrade model attention—a phenomenon known as Context Poisoning and Memory Drift.

Turn 1-3: Pristine Context ──> 98% Accuracy (Strict Adherence to System Prompt)
Turn 8-12: Context Noise Floods Prompt ──> Attention Distraction & Sub-Goal Drift
Turn 15+: Context Poisoning ──> False Fact Reinforcement ──> Destructive Execution Cascade

When an agent produces a slight inaccuracy at step 4 (such as assuming a column name exists in a database), that error is appended to the message history. By step 12, the model treats its own past hallucination as ground-truth historical evidence, resulting in runaway state corruption or infinite repetition loops.

The Architecture of Context Poisoning

To understand why large context windows fail on complex, long-running agent tasks, we must analyze the three distinct failure modes that emerge in multi-turn execution:

1. Attention Dilution (The "Lost in the Middle" Multiplier)

Even with advanced rotary position embeddings, transformer attention is non-uniform. When a context window is loaded with 40,000 tokens of raw bash outputs and web page HTML, the model's ability to retrieve and respect strict negative constraints declared in the system prompt drops precipitously.

2. Phantom History Reinforcement

If an agent attempts a tool call with incorrect parameters and receives an error message, the entire failure loop remains in the context history. Subsequent turns frequently pattern-match against the failed attempts rather than the original system instructions, leading the agent into repetitive trial-and-error spirals.

3. Indirect Prompt Injection via Tool Returns

When agents scrape external web pages, parse customer tickets, or inspect vendor PDFs, adversarial or unformatted text is ingested directly into the context window. Without pre-injection sanitization, malicious instructions embedded in third-party data can hijack the agent's goal trajectory.

The 4 Pillars of Deterministic Context Harnesses

At MustAdaptAI, we design and deploy hardened agent harnesses that maintain laboratory-grade precision across arbitrarily long workflows:

[ Raw Tool Output (15k Tokens) ]
               │
               ▼
[ Pre-Context Sanitizer & Extractor ] ──(Strip ANSI, HTML, PII & Noise)
               │
               ▼
[ Sliding-Window State Compactor ] ──(Extract Structured State Delta: 350 Tokens)
               │
               ├──> Immutable System Constraints (Pinned at Top)
               ├──> Current Canonical State (Pydantic / Zod Schema)
               └──> Ephemeral Turn Scratchpad (Bounded to Last 3 Turns)
               │
               ▼
[ Monotonic Progress Gate ] ──(Assert State Advance ≠ 0; If Loop: Abort & Rollback)

1. Sliding-Window Context Compaction

Never preserve raw conversational histories beyond the immediate 3 turns. After each tool execution, a lightweight extraction pass summarizes the outcome into a deterministic, typed state schema (e.g., {"files_modified": [...], "current_test_status": "failing_assert_2"}), discarding thousands of tokens of ephemeral noise.

2. Strict State-Boundary Isolation

We partition the context window into three air-gapped zones:

• Zone 1: Immutable Core (System Rules): High-priority governance policies, security boundaries, and tool definitions that are never compressed or truncated.
• Zone 2: Canonical State Ledger: A structured, continuously updated JSON snapshot representing verified ground truth.
• Zone 3: Ephemeral Scratchpad: A strictly bounded FIFO buffer containing only the current turn's active reasoning and immediate tool feedback.

3. Pre-Context Tool Sanitization

External tool outputs must pass through an isolation filter before being appended to the context. We strip terminal escape sequences, truncate repetitive stack traces to the top 5 frames, and run entity extraction on HTML responses rather than injecting raw markup.

4. Monotonic Progress Assertions & Circuit Breakers

To eliminate runaway infinite loops, every turn evaluates a mathematical state delta. If the agent executes 3 consecutive actions without advancing the canonical state ledger, the circuit breaker trips, halting execution and notifying a human operator with an audited snapshot.

Measurable Impact on Enterprise Agent Workflows

Organizations implementing structured context compaction harnesses achieve:

• 92% reduction in per-workflow token consumption, slashing API costs from 4.50/run to 0.35/run.
• Zero cascading hallucination outages across multi-step data engineering and DevOps pipelines.
• Sub-3-second inference turnaround by keeping active prompt sizes consistently below 8,000 tokens regardless of task complexity.