AuraGuard

Deterministic middleware for AI agents: stop tool loops, duplicate side-effects, retry storms. Open source.

[AuraGuard @aura_guard]

What the report looks like:

Run efficiency: 72.0% productive (18 executed, 7 denied)
Cost: $0.14 spent / $1.00 budget
Side-effects: 1 executed, 1 blocked

Interventions:
[4x] repeat_detection
[2x] jitter_detection
[1x] side_effect_gating

[AuraGuard @aura_guard]

AuraGuard v0.7.1

Now with a full run report — see exactly what got blocked, cached, rewritten, and why.

Plus: single-file standalone. No install needed.

curl -O https://t.co/OE7GcEzHfa

[AuraGuard @aura_guard]

@github Three failure modes I see repeatedly:
1. Sequence loops: A delegates to B, B to A, forever
2. Retry storms: tool 429s, agent retries in a loop
3. Duplicate mutations: refund timeout, retry, double refund

max_steps can't distinguish productive calls from loops.

[AuraGuard @aura_guard]

@MCP Works with any MCP client — Claude Desktop, Cursor, Windsurf, OpenAI Agents SDK, and more.

8 primitives. 71 tests. Zero false positives. Zero dependencies in core.

https://t.co/RAjDYiUKC6

[AuraGuard @aura_guard]

AuraGuard now works with MCP.

Any FastMCP server → wrap with GuardedMCP → every tool call gets loop detection, duplicate side-effect prevention, and cost budgets. Automatically.

pip install aura-guard[mcp]

v0.5.0 shipped today.

[AuraGuard @aura_guard]

What it looks like:

@mcp.tool()
def refund(order_id, amount):
return payments.refund(order_id, amount)

That's it. Every call checked automatically.
Duplicate refund? Blocked.
Retry storm? Circuit-broken.
Budget exceeded? Escalated.

[AuraGuard @aura_guard]

@twtofsahil Classic retry loop. We measured this exact pattern: agent retrying a failing tool burns $0.045/min on cheap models. 47 min = roughly $2 on Haiku, way more on GPT-4. Hashing each tool call before executing and returning cached results on duplicates would have caught it in seconds.

[AuraGuard @aura_guard]

@kurochenko "Agent managing an agent" can loop too. We tested two agents monitoring each other - 60 rounds of ping-pong before our cap stopped them. Cheaper fix: hash every tool call, return cached result on duplicates. Catches stuck agents without needing a second agent.

[AuraGuard @aura_guard]

@eng_ahmedsayed We measured this exactly. Two agents looping: $0.045/min, $2.68/hr. Budget caps catch it eventually but only after damage. Hashing each tool call before executing and returning cached results on duplicates killed a 60-round loop at round 7. Pattern detection > budget caps.

[AuraGuard @aura_guard]

Full writeup with both experiments and cost breakdowns:
I Spent $0.20 Reproducing the Multi-Agent Loop That Cost Someone $47K | by Msatfi89 | Feb, 2026 | Medium

Open source: https://t.co/RAjDYiVirE

[AuraGuard @aura_guard]

We spent $0.20 reproducing the multi-agent loop that reportedly cost someone $47K.

Two agents. No termination condition. 60 rounds. $0.16 burned. Zero useful output.

Extrapolated: $5,650 over 11 days at GPT-4o pricing.

[AuraGuard @aura_guard]

Second test: no forced prompts.

One agent, one search tool that returns "no results." Normal prompt.

Without guard: 24 searches, $0.024
With guard: 9 searches, $0.006

The agent was doing its job. It just needed something to say "stop."