Your AI Agent Is Eating Its Own Tail. Let's Stop It.
Autonomous agents trapped in recursive self-referential loops produce zero external output. Every cycle is spent inspecting their own scoring, state, and internal metrics instead of doing real work. This sprint breaks that loop — permanently.
3 out of 3 market-research heads are running self-referential Milo-meta-research queries. Zero competitor analysis. Zero niche identification. Zero product output. The system is optimizing for internal consistency instead of external value — and it will not stop on its own.
What You Get
5 concrete artefacts — not decks, not advisories, not PDFs full of platitudes.
Numbered Incident Report
PDF, 12–20 pages. Root-cause chain traced from trigger event through recursive self-inspection queries to observed failure state. Includes timeline, affected nodes, and call-graph of the meta-research loop.
Replay Fixture
Deterministic test case in Python. Reproduces the exact recursive loop sequence from the incident report so your team can validate any fix against a known failure mode — indefinitely.
Negative Prompting Guide
Production-ready prompt-engineering cookbook. Explicit block-lists, constraint syntax, and fallback routing rules that prevent any agent from generating self-referential queries going forward. Copy-paste-ready.
Pre-Flight Validator
Schema-validation YAML with accompanying runner script. Any research plan must pass the external-entity check — competitor, niche, or product as primary subject — before the agent receives it. Configurable thresholds.
Context Pruning Playbook
Step-by-step operational guide with code snippets for context-window pruning, entity disambiguation (Milo AI vs. Nestlé Milo), and a fresh-initialization protocol to break existing loops immediately on deployment.
How It Works
5 business days. Diagnose → Reproduce → Patch → Validate → Handoff.
Frequently Asked
Is $3,000 fixed or are there variables?
Fixed. The sprint is scoped at $3,000 for this specific failure mode: autonomous research agents looping on self-referential queries. If your issue involves additional agent types, multiple simultaneous loops, or a more complex architecture, we can scope a custom engagement — the research will identify the severity before you commit.
Do I need to share raw agent logs or internal system access?
Yes — the incident report requires access to query logs, context history, and instrumentation traces from the affected agent(s). Logs can be anonymized if they contain proprietary data. We use a secure upload link and do not retain copies after delivery. No external system access is needed; the replay fixture is built from exported data, not live system interaction.
What if the replay fixture doesn't actually reproduce the loop?
The deliverables are built against the data you provide. If the logs are incomplete or the loop involves non-deterministic timing, the fixture may require additional instrumentation data. If we cannot produce a passing fixture within the 5-day window, we extend the sprint at no additional charge until we can — or issue a partial refund for the undelivered items.
Can this sprint prevent loops in other agents, not just the one affected?
The negative prompting guide and pre-flight validator are architected to be agent-agnostic. The constraint syntax applies to any LLM-based autonomous agent. The playbook for context-window pruning works across agent frameworks. The incident report is specific to the affected agent, but the defensive artefacts are designed for broad reuse.