Aegis

Turn unstable AI into reliable systems.

Aegis is a runtime control layer that predicts instability and returns control plans to stabilize AI behavior — no retraining or prompt rewrites required.

pip install scelabs-aegis

---

🚀 10-second integration

from aegis import AegisClient
import openai

client = AegisClient()

plan = client.auto(
    system_type="multi_agent",
    base_prompt="You are a support system.",
    symptoms=["agents_disagree"],
    severity="medium",
)

response = openai.chat.completions.create(
    **plan.for_openai(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a support system."},
            {"role": "user", "content": "Handle this case."}
        ],
    )
)

That’s it.

---

🧠 What just happened

Aegis analyzed your system and returned a control plan:

• adjusted generation behavior (temperature, top_p)
• stabilized prompt behavior
• improved coordination
• reduced variability

You didn’t rewrite anything.

---

🔌 Works everywhere

Aegis plugs into any AI system.

OpenAI

plan.for_openai(...)

LangChain

cfg = plan.for_langchain(messages=messages)

LangGraph

state = plan.apply_to_state(state)

HuggingFace (local / small models)

cfg = plan.for_huggingface(prompt="Handle this clearly")

---

⚡ Example: Real Workflow Impact

This demo runs the same multi-agent workflow twice:

• once as baseline
• once with Aegis applied at runtime

Both reach the same correct final answers.

The difference is how efficiently they get there.

📊 Results

Metric	Baseline	Aegis
Final Accuracy	1.0	1.0
Lane Accuracy	0.83	1.0
Efficiency	0.82	1.0
LLM Calls	44	32
Verifier Calls	11	8
Replans	5	2
Cost	$0.00583	$0.003946

🔥 What changed

• Same outcomes
• Fewer steps
• Fewer retries
• Better routing
• Lower cost

🧠 Takeaway

Aegis does not change what your system decides.

It changes how your system behaves while deciding.

Same system. Better execution.

---

---

🧠 Local / Smaller Model Example

Aegis makes smaller or local models usable in real systems.

from transformers import pipeline
from aegis import AegisClient

client = AegisClient()
pipe = pipeline("text-generation", model="gpt2")

plan = client.auto(
    system_type="single_agent",
    base_prompt="Provide clear reasoning.",
    symptoms=["inconsistent_outputs"],
    severity="medium",
)

cfg = plan.for_huggingface(
    prompt="Explain this clearly and step by step."
)

output = pipe(cfg["prompt"], **cfg["model_kwargs"])
print(output)

Result:

• clearer reasoning
• more consistent outputs
• fewer retries

---

🧠 Advanced usage (Plan API)

plan = client.plan(
    system_type="single_agent",
    base_prompt="You are a support assistant.",
    symptoms=["inconsistent_outputs"],
    severity="medium",
)

print(plan.prediction)
print(plan.controls)

---

🛠 Tool Control Example

Ensure your AI uses tools correctly.

plan = client.auto(
    system_type="multi_agent",
    base_prompt="You must use tools correctly.",
    symptoms=["tool_misuse"],
    severity="medium",
)

print(plan.tool_config())

Result:

• correct tool usage
• fewer failures
• higher task success

---

💡 What Aegis does

Aegis analyzes instability and returns a control plan that adjusts:

• prompts
• generation behavior
• coordination rules
• validation and retry behavior
• tool usage

Result:

• more consistent outputs
• fewer retries
• better decision-making
• more reliable systems

---

🔥 Why Aegis

• Works instantly with your existing setup
• No retraining required
• Reduces retries and debugging
• Improves edge-case handling
• Makes AI systems production-ready

---

💰 Cost & Efficiency

Aegis improves first-pass success:

• fewer retries
• lower API cost
• more predictable behavior

In many cases:

Aegis pays for itself by reducing retries alone.

---

🧠 Use Cases

Customer Support

Handle edge cases consistently without breaking policy.

Tool Calling / Agents

Ensure correct tool usage and execution.

Structured Output

Reduce invalid responses and retry loops.

Multi-Agent Systems

Prevent disagreement and coordination drift.

Local / Smaller Models

Make them reliable and usable.

---

📦 Installation

pip install scelabs-aegis

After installing, request an API key through the onboarding endpoint and set AEGIS_API_KEY before making calls.

---

🔑 Onboarding

1. Request an API key

curl -X POST "$AEGIS_URL/v1/onboard" \
  -H "Content-Type: application/json" \
  -d '{"account_name":"My Team","email":"you@example.com"}'

---

2. Set your API key

export AEGIS_API_KEY=your_api_key

Optional:

export AEGIS_URL=https://your-aegis-url

---

3. You're ready

from aegis import AegisClient
import os

client = AegisClient(
    api_key=os.environ["AEGIS_API_KEY"],
    base_url=os.getenv("AEGIS_URL"),
)

---

🧪 Examples

python examples/openai_basic.py
python examples/langchain_basic.py
python examples/langgraph_basic.py
python examples/huggingface_basic.py

---

🚀 Full Demo (Multi-Agent Workflow)

https://github.com/SCELabs/aegis-agent-workflow-demo

---

🧩 Design

Aegis is:

• thin
• backend-driven
• runtime-focused
• production-oriented

It does NOT:

• replace your model
• require retraining
• expose internal engine complexity

---

License

MIT