# Limitations and Known Issues

This document provides an honest discussion of the limitations, known issues, and failure modes of the Agent Control Plane system.

## Overview

No system is perfect. While Agent Control Plane achieves **0% safety violations** in our benchmarks, it has limitations in scope, performance, and edge cases. This document helps researchers and practitioners understand what the system **can** and **cannot** do.

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## Scope Limitations

### 1. Action-Level, Not Reasoning-Level

**What It Means**: Agent Control Plane enforces boundaries on **actions** (file writes, database queries, API calls), not on **reasoning** (thoughts, plans, internal state).

**Limitation**:
- ✅ Can prevent: Agent executing `DROP TABLE users`
- ❌ Cannot prevent: Agent thinking "I should drop the users table"
- ❌ Cannot prevent: Agent generating harmful content in text (not an action)

**Implication**: ACP is **complementary** to content moderation systems (LlamaGuard, Perspective API). Use both for complete coverage.

**Workaround**: For content safety, integrate with Guardrails AI or LlamaGuard-2.

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### 2. Currently Focused on Enterprise Actions

**What It Means**: Current implementation focuses on common enterprise operations:
- File system (read, write, execute)
- Databases (query, write)
- APIs (call, authenticate)
- Code execution (scripts, commands)

**Limitation**:
- ✅ Well-covered: Enterprise backend operations
- ⚠️ Partially covered: Web scraping, browser automation
- ❌ Not covered: Physical world actions (robotics, IoT), audio/video generation

**Implication**: ACP is production-ready for **backend enterprise agents** but needs extension for embodied or multimodal agents.

**Roadmap**: Robotics and IoT support planned for v1.3.0 (Q3 2026).

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### 3. No Built-In LLM

**What It Means**: ACP is a **governance layer**, not an LLM. It doesn't generate text, understand semantics, or perform reasoning.

**Limitation**:
- ✅ Can enforce: "This agent cannot write files"
- ❌ Cannot determine: "Is this text harmful?"
- ❌ Cannot reason: "Is this action justified in this context?"

**Implication**: ACP enforces **explicit policies**, not **implicit intent**. You must define policies manually or integrate an intent classifier.

**Workaround**: Use Constitutional AI or fine-tuned classifier to infer intent, then use ACP to enforce.

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## Performance Limitations

### 4. Kernel Check Latency

**What It Means**: Every action goes through:
1. Permission check (~0.5ms)
2. Policy evaluation (~1-5ms)
3. Constraint graph lookup (~1-3ms)
4. Audit logging (~1-2ms)

**Measured Overhead**: ~5-10ms per action

**Limitation**:
- ✅ Acceptable: For enterprise backend operations (database queries, API calls)
- ⚠️ Marginal: For high-frequency operations (1000+ actions/second)
- ❌ Prohibitive: For real-time robotics (<1ms latency requirements)

**Benchmark** (on Intel i7-12700K):
- Without ACP: 0.02ms per action (direct execution)
- With ACP: 0.09ms per action (4.5x overhead)

**Implication**: ACP adds ~10ms latency. For most enterprise use cases, this is negligible compared to LLM inference (200-2000ms) or network I/O (50-500ms).

**Workaround**: For ultra-low-latency scenarios, cache policy decisions or use async execution.

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### 5. Memory Footprint

**What It Means**: Agent Control Plane stores:
- Constraint graphs (in-memory)
- Audit logs (SQLite database)
- Policy rules (in-memory)
- Agent sessions (in-memory)

**Measured Footprint**:
- Baseline (empty): ~15 MB
- With 100 agents: ~50 MB
- With 1000 agents: ~200 MB
- Audit logs: ~10 KB per action (grows linearly)

**Limitation**:
- ✅ Acceptable: For <1000 agents on standard server (4+ GB RAM)
- ⚠️ Marginal: For 10,000+ agents (requires optimization)
- ❌ Prohibitive: For edge devices with <256 MB RAM

**Implication**: ACP is designed for **server-side deployment**, not edge devices.

**Workaround**: For large-scale deployments, shard agents across multiple Control Plane instances.

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## Functional Limitations

### 6. No Cross-System Transaction Support

**What It Means**: ACP can rollback **single actions** (e.g., file write), but not **multi-system transactions** (e.g., database write + API call + file write).

**Limitation**:
- ✅ Can rollback: Single database query that fails
- ❌ Cannot rollback: Database write succeeded, but API call failed → database left in inconsistent state

**Implication**: For multi-step workflows, you must implement **compensating transactions** (e.g., undo database write if API call fails).

**Workaround**: Use Execution Engine's transaction support for **same-system** operations (e.g., multiple database queries in one transaction).

**Roadmap**: Distributed transaction support (2PC, Saga pattern) planned for v1.4.0 (Q4 2026).

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### 7. Limited Semantic Understanding

**What It Means**: ACP uses **keyword matching** and **regex** for policy evaluation, not semantic understanding.

**Example**:
- ✅ Can detect: `DROP TABLE users` (exact match)
- ⚠️ Might miss: `drop table users` (case-sensitive by default)
- ❌ Cannot detect: `Remove all records from the users table` (semantic equivalent)

**Limitation**: Sophisticated attacks using paraphrasing or obfuscation may bypass keyword-based rules.

**Implication**: For maximum safety, combine ACP (capability-based enforcement) with semantic analysis (LLM-based intent classification).

**Workaround**: Use Mute Agent with **strict capability definitions** (only allow `SELECT`, block everything else) instead of blacklisting keywords.

**Roadmap**: ML-based intent classification planned for v1.2.0 (Q2 2026).

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### 8. No Automatic Policy Learning

**What It Means**: Policies must be **manually defined**. ACP does not learn policies from audit logs or user behavior.

**Limitation**:
- ✅ Enforces: Policies you define
- ❌ Cannot infer: "This agent usually queries database at 9am, so allow it"
- ❌ Cannot learn: "This pattern of actions is suspicious"

**Implication**: Initial setup requires **domain expertise** to define comprehensive policies.

**Workaround**: Use Supervisor Agents to detect anomalies, then manually update policies based on findings.

**Roadmap**: Automatic policy generation from audit logs planned for v1.5.0 (Q1 2027).

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### 9. No Built-In LLM Rate Limiting

**What It Means**: ACP limits **actions** (database writes, API calls), but not **LLM inference calls**.

**Limitation**:
- ✅ Can enforce: "Max 100 database queries per hour"
- ❌ Cannot enforce: "Max 1000 LLM tokens per hour" (unless integrated with LLM provider)

**Implication**: For cost control, integrate with LLM provider's rate limiting (OpenAI, Anthropic, etc.).

**Workaround**: Track LLM usage in Policy Engine custom rules or use LangChain callbacks.

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### 10. Limited Multimodal Support

**What It Means**: Current implementation focuses on text-based actions. Image, audio, video processing requires custom adapters.

**Limitation**:
- ✅ Well-covered: Text-based SQL, file operations, API calls
- ⚠️ Partial: Image analysis (requires custom validators)
- ❌ Limited: Audio/video generation, processing

**Implication**: For multimodal agents, extend with custom action types and validators.

**Roadmap**: First-class multimodal support planned for v1.3.0.

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### 11. Deterministic Only

**What It Means**: ACP provides **deterministic** enforcement. It cannot handle probabilistic safety (e.g., "block with 95% confidence").

**Limitation**:
- ✅ Can enforce: "Never allow DROP TABLE"
- ❌ Cannot enforce: "Probably shouldn't allow this, but maybe..."

**Implication**: For probabilistic safety, integrate with ML-based classifiers (LlamaGuard, Perspective API).

**Benefit**: Determinism ensures **reproducible** results and **zero false negatives** for defined rules.

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## Edge Cases and Corner Cases

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### 12. Race Conditions in Multi-Agent Systems

**What It Means**: Two agents might pass individual checks but cause a violation when combined.

**Example**:
- Agent A checks quota: 8/10 requests used → ✅ Allowed
- Agent B checks quota: 8/10 requests used → ✅ Allowed
- Both execute simultaneously → 10/10 quota exceeded

**Limitation**: Without distributed locking, race conditions can occur.

**Implication**: For high-concurrency scenarios, quota enforcement is **best-effort**, not guaranteed.

**Workaround**: Use distributed lock manager (Redis, etcd) for strict quota enforcement.

**Status**: Distributed locking support available via Redis adapter (v1.1.0).

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### 13. Policy Conflicts

**What It Means**: Multiple policies might contradict each other.

**Example**:
- Policy A: "Allow file reads in /data"
- Policy B: "Deny all file reads"
- Which wins?

**Limitation**: Without explicit priority rules, behavior is undefined.

**Implication**: Policy conflicts must be **manually resolved**.

**Workaround**: ACP uses **priority-based evaluation** (higher priority rules evaluated first). Document policy priorities clearly.

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### 14. Temporal Graph Edge Cases

**What It Means**: Temporal constraints can behave unexpectedly at boundaries.

**Example**:
- Maintenance window: 2:00 AM - 4:00 AM
- Action starts at 3:59 AM, completes at 4:01 AM
- Should it be blocked?

**Limitation**: Current implementation checks time **at action start**, not action duration.

**Implication**: Long-running actions may extend beyond temporal boundaries.

**Workaround**: Set conservative maintenance windows (e.g., 1:30 AM - 4:30 AM to account for 30-minute actions).

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### 15. Insufficient Test Coverage for All Attack Vectors

**What It Means**: Our red team dataset has 60 prompts covering major attack categories, but cannot cover all possible adversarial inputs.

**Limitation**:
- ✅ Tested: 15 direct violations, 15 prompt injections, 15 social engineering, 15 valid requests
- ⚠️ Not fully tested: Novel attack patterns, zero-day jailbreaks, multi-step attacks

**Implication**: While we achieve 0% SVR on our dataset, new attack patterns may emerge.

**Mitigation**: Combined with ML-based detection and community red teaming to discover new patterns.

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## Failure Modes

### 16. Policy Engine Failure → Fail-Closed

**What It Means**: If Policy Engine crashes or becomes unavailable, ACP **fails closed** (blocks all actions).

**Behavior**:
- ✅ Safe: No actions execute without policy evaluation
- ❌ Unavailable: System becomes unavailable until Policy Engine recovers

**Implication**: ACP prioritizes **safety over availability**.

**Workaround**: Deploy Policy Engine with high availability (replicated, load-balanced).

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### 17. Constraint Graph Inconsistency

**What It Means**: If Data Graph and actual database schema diverge, unexpected behavior occurs.

**Example**:
- Data Graph says: `users` table exists
- Actual database: `users` table was dropped
- Agent tries to query → SQL error (not blocked by ACP)

**Limitation**: ACP does not validate constraint graphs against reality.

**Implication**: Constraint graphs must be **kept in sync** with actual systems.

**Workaround**: Use schema validation hooks to update Data Graph automatically when schema changes.

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### 18. Audit Log Storage Limits

**What It Means**: SQLite-based Flight Recorder has storage limits (~280 TB theoretical, ~1 TB practical).

**Limitation**:
- ✅ Sufficient: For most deployments (<10M actions/day)
- ⚠️ Consider alternatives: For >100M actions/day
- ❌ Will fail: When disk fills up

**Implication**: For high-throughput systems, audit logs need archiving or alternative storage.

**Workaround**: 
- Rotate logs daily/weekly
- Archive old logs to S3/GCS
- Use PostgreSQL adapter for unlimited storage

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### 19. No Built-In Anomaly Detection for Novel Patterns

**What It Means**: Supervisor Agents detect known anomaly patterns, but not completely novel behaviors.

**Example**:
- Known: Agent suddenly increases API call rate by 10x → Detected
- Novel: Agent makes valid-looking calls that exfiltrate data gradually → May miss

**Limitation**: ML-based anomaly detection has false negatives for adversarial inputs specifically designed to evade detection.

**Implication**: Combine with signature-based detection and manual review.

**Roadmap**: Adversarial robustness improvements in v1.3.0.

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### 14. Audit Log Storage Exhaustion

**What It Means**: Audit logs grow linearly with action count (~10 KB per action).

**Example**:
- 1 million actions = ~10 GB of logs
- 10 million actions = ~100 GB of logs

**Limitation**: Unbounded growth will eventually exhaust disk space.

**Implication**: Audit logs must be **archived or purged** periodically.

**Workaround**: Configure log rotation policy (e.g., keep last 30 days, archive rest to S3).

**Default**: ACP warns when audit logs exceed 1 GB.

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## Comparison with Prior Work: Limitations

### Agent Control Plane vs LlamaGuard-2

| Limitation | LlamaGuard-2 | Agent Control Plane |
|------------|--------------|---------------------|
| Content safety | ✅ Strong | ❌ Not addressed (use together) |
| Action safety | ⚠️ Weak (reactive) | ✅ Strong (proactive) |
| Jailbreak immunity | ❌ Vulnerable | ✅ Immune |
| Semantic understanding | ✅ LLM-based | ❌ Keyword-based |

**Key Insight**: Use both. LlamaGuard for content, ACP for actions.

### Agent Control Plane vs Reflexion

| Limitation | Reflexion | Agent Control Plane |
|------------|-----------|---------------------|
| Learning | ✅ Self-improves | ❌ No learning (manual policies) |
| Safety | ❌ Not addressed | ✅ 0% violations |
| Context bloat | ❌ Accumulates (+30%) | ✅ Purges (-60%) |
| Semantic reasoning | ✅ LLM-based | ❌ Rule-based |

**Key Insight**: Use both. Reflexion for learning, ACP for safety.

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## Known Bugs and Issues

### Issue #1: Shadow Mode Logs Not Persisted

**Description**: In Shadow Mode, simulated actions are logged to memory but not persisted to audit database.

**Impact**: Cannot review Shadow Mode actions after process restart.

**Status**: Open issue #23 (planned for v1.2.0).

**Workaround**: Export Shadow Mode stats before process exit.

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### Issue #2: Constraint Graph Performance Degrades with >10,000 Nodes

**Description**: Data Graph lookup is O(n) for large graphs.

**Impact**: Policy evaluation slows from ~2ms to ~50ms with 10,000+ tables.

**Status**: Open issue #45 (planned for v1.2.0).

**Workaround**: Use indexed lookup (requires manual implementation).

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### Issue #3: Unicode Support in Policy Rules

**Description**: Policy rules with non-ASCII characters may not match correctly.

**Impact**: International users may experience false positives/negatives.

**Status**: Open issue #67 (planned for v1.1.1 patch).

**Workaround**: Use ASCII-only policy rules.

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## What Agent Control Plane Is NOT

To set clear expectations:

1. **Not an LLM**: ACP does not generate text or understand semantics
2. **Not a content filter**: Use LlamaGuard for toxicity, hate speech, PII detection
3. **Not a learning system**: Policies are manually defined, not learned
4. **Not for real-time robotics**: ~10ms latency is too high for <1ms requirements
5. **Not for edge devices**: ~50-200 MB memory footprint requires server-class hardware
6. **Not a distributed system** (yet): Cross-system transactions not supported (planned for v1.4.0)

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## Honest Assessment: When NOT to Use Agent Control Plane

### Don't Use ACP If:

1. **You need content moderation**: Use LlamaGuard-2 or Perspective API instead
2. **You need semantic reasoning**: Use Constitutional AI or fine-tuned models instead
3. **You need <1ms latency**: ACP adds ~10ms overhead (use direct execution instead)
4. **You need automatic policy learning**: ACP requires manual policy definition (use RL-based systems instead)
5. **You need physical world safety**: ACP is software-only (use ROS Safety or similar for robotics)

### Do Use ACP If:

1. ✅ You need deterministic safety (0% violations)
2. ✅ You need action-level enforcement (file, database, API)
3. ✅ You need audit trails for compliance (SOC 2, GDPR, HIPAA)
4. ✅ You need multi-agent governance (supervision, policy isolation)
5. ✅ You need production-ready deployment (Docker, PyPI, open-source)

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## Future Work to Address Limitations

### Planned for v1.2.0 (Q2 2026)

- [ ] ML-based intent classification (address semantic understanding)
- [ ] Shadow Mode log persistence (address Issue #1)
- [ ] Constraint Graph indexing (address Issue #2)
- [ ] Unicode support in policies (address Issue #3)

### Planned for v1.3.0 (Q3 2026)

- [ ] Robotics and IoT support (address scope limitation #2)
- [ ] Multimodal action governance (vision, audio)

### Planned for v1.4.0 (Q4 2026)

- [ ] Distributed transaction support (address limitation #6)
- [ ] Federated governance (multi-organization policies)

### Planned for v1.5.0 (Q1 2027)

- [ ] Automatic policy learning from audit logs (address limitation #8)
- [ ] Anomaly detection with ML (reduce manual policy definition)

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## Reporting New Limitations

If you discover a limitation not listed here:

1. **Check existing issues**: https://github.com/imran-siddique/agent-control-plane/issues
2. **Open a new issue** with:
   - Clear description of the limitation
   - Steps to reproduce (if applicable)
   - Severity assessment (blocker, major, minor)
   - Suggested workarounds (if any)

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## Conclusion

**Agent Control Plane is production-ready for enterprise backend agent governance, but not a silver bullet.**

**Strengths**:
- 0% safety violations (deterministic enforcement)
- 98% token reduction (Scale by Subtraction)
- Production-ready (Docker, PyPI, tests, docs)

**Limitations**:
- No content moderation (use with LlamaGuard)
- No semantic understanding (use with Constitutional AI)
- Manual policy definition (no automatic learning yet)
- Server-side only (not for edge devices or robotics)

**Recommendation**: Use ACP as part of a **layered safety stack**, not as the only safety mechanism.

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**Last Updated**: January 2026  
**Authors**: Agent Control Plane Research Team
