scelabs-aegis 0.4.0

Runtime control layer for stabilizing AI systems and improving behavior without retraining

Runtime control for AI systems.

Aegis sits on top of your AI pipeline and returns structured control decisions that stabilize behavior at runtime without replacing your model, agent, or retrieval system.

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Why Aegis

Modern AI systems often fail in subtle but costly ways:

• inconsistent outputs across similar inputs
• unstable multi-step reasoning
• retrieval drift in RAG systems
• fragile workflow and agent execution

Aegis addresses these problems with runtime control, not retraining, fine-tuning, or model swapping.

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Core Idea

Aegis is a control layer, not an execution layer.

from aegis import AegisClient

client = AegisClient(api_key="YOUR_API_KEY")

result = client.auto().llm(...)

Aegis will:

• detect instability signals
• select minimal corrective actions
• return runtime controls and observability data

Aegis does not execute the downstream LLM call for you. It is not a model, not a full execution engine, and not a replacement for LangChain, LangGraph, or your tool stack.

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Installation

pip install scelabs-aegis

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Get an API Key

curl -X POST https://aegis-backend-production-4b47.up.railway.app/v1/onboard \
  -H "Content-Type: application/json" \
  -d '{"email":"[e-mail]"}'

This returns:

• api_key
• auto scope URLs (including auto_llm_url, auto_rag_url, auto_step_url, auto_context_url, and auto_agent_url on current backends)
• example usage

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Set Environment

export AEGIS_API_KEY=your_key_here
export AEGIS_BASE_URL=https://aegis-backend-production-4b47.up.railway.app

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First Call

from aegis import AegisClient, AegisConfig

client = AegisClient(
    config=AegisConfig(mode="balanced"),
)

result = client.auto().llm(
    base_prompt="You are a careful assistant.",
    input={"user_query": "Explain recursion simply."},
    symptoms=["inconsistent_outputs"],
    severity="medium",
)

print(result.actions)
print(result.explanation)
print(result.scope_data)

---

Scope-First API

Aegis uses a scope-first runtime interface:

client.auto().llm(...)
client.auto().rag(...)
client.auto().step(...)
client.auto().context(...)
client.auto().agent(...)

These calls map to first-class public backend routes:

• POST /v1/auto/llm
• POST /v1/auto/rag
• POST /v1/auto/step
• POST /v1/auto/context
• POST /v1/auto/agent

---

Scopes

LLM

Use llm when you need stabilization around a direct model call.

result = client.auto().llm(
    base_prompt="You are a careful assistant.",
    input={"user_query": "Explain recursion simply."},
    symptoms=["inconsistent_outputs"],
    severity="medium",
)

---

RAG

Use rag when instability appears in retrieval plus generation.

result = client.auto().rag(
    query="What changed in the policy?",
    retrieved_context=[
        "Policy updated last week.",
        "Refund window reduced to 14 days."
    ],
    symptoms=["retrieval_drift"],
    severity="medium",
)

What changed in RAG

Aegis no longer treats retrieval as a fixed input.

It controls retrieval behavior at runtime.

The RAG scope now:

• enforces typed evidence coverage (source, test, support)
• applies relevant-file protection (never drops critical context)
• performs selective expansion (not always-on)
• removes noise without losing required files
• uses staged retrieval only when ambiguity or gaps are detected
• applies guided retrieval (intent + plan) only when justified

This is not just ranking or filtering.

Aegis:

• evaluates the retrieved set
• diagnoses issues (missing support, ambiguity, distractors)
• applies minimal corrective actions
• returns a controlled context for downstream use

---

How RAG control works

At runtime:

1. You pass query + retrieved context

2. Aegis evaluates:

   • missing required evidence
   • role imbalance (source/test/support)
   • distractor pressure
   • ambiguity / multi-branch cases

3. It decides whether to:

   • keep as-is
   • prune noise
   • expand retrieval
   • run a staged second pass
   • guide retrieval when needed

4. It enforces relevant-file protection before final selection

Everything is gated and minimal.

No always-on expansion. No blind pruning.

---

Works with Agentic RAG

Yes.

Aegis sits above your agent system and stabilizes retrieval behavior.

It can:

• prevent agents from drifting due to poor context
• enforce evidence requirements before execution
• reduce retries and replans
• stabilize multi-step retrieval chains

Aegis does not replace your agents — it makes them more reliable.

---

Step

Use step when you need stabilization for a workflow or agent step.

result = client.auto().step(
    step_name="coordinator",
    step_input={"task": "resolve ticket"},
    symptoms=["unstable_workflow"],
    severity="medium",
)

Context

Use context to control information state before the next model or workflow action.

result = client.auto().context(
    objective="Prepare the next response context.",
    messages=[
        {"role": "user", "content": "Summarize blockers from this thread."},
        {"role": "assistant", "content": "Draft summary goes here."},
    ],
    tool_results=[
        {"tool": "ticket_lookup", "ok": True, "data": {"id": "T-42", "status": "open"}},
    ],
    constraints=["keep it concise", "cite ticket IDs"],
    severity="medium",
)

context can clean and prioritize messages and tool results so your downstream call receives better state.

Agent

Use agent to control multi-step workflow loops on top of your existing AI pipeline.

result = client.auto().agent(
    goal="Resolve the support ticket safely.",
    steps=[
        {"name": "triage", "input": {"ticket_id": "T-42"}},
        {"name": "propose_resolution", "input": {"channel": "email"}},
    ],
    max_steps=4,
    severity="medium",
)

agent can control multi-step execution, tool-result integration, carry-forward context, and stop/retry/escalation decisions.

---

What Aegis Returns

Every call returns an AegisResult.

result = client.auto().llm(...)

Key fields

• actions — interventions Aegis selected
• trace — structured control trace
• metrics — runtime signals
• used_fallback — whether fallback behavior was used
• explanation — concise rationale
• scope — llm, rag, step, context, or agent
• scope_data — scope-specific runtime data

---

RAG Observability (new)

RAG responses now include richer runtime signals:

Inside scope_data:

• public_rag_runtime — high-level runtime info
• retrieval_intent — if guided retrieval was used
• retrieval_plan — structured retrieval guidance (when triggered)
• initial_retrieved_chunks — stage 1 candidates
• stage2_retrieved_chunks — staged retrieval results (if used)
• before_after_metrics — context quality changes

Inside trace:

• decision.policy_path includes:

  • expansion score / threshold
  • staged retrieval activation
  • intent / plan activation

• changes includes:

  • protected chunk IDs
  • relevant-file protection indicators

These are optional but useful for debugging pipeline behavior.

---

Typical RAG Integration Pattern

result = client.auto().rag(
    query="Why is retry failing?",
    retrieved_context=raw_context,
    symptoms=["retrieval_drift"],
    severity="medium",
)

controlled_context = result.scope_data.get("retrieved_context")
trace = result.trace

print(controlled_context)
print(result.actions)
print(trace)

You apply the returned controlled context in your downstream system.

---

Example Result Shape

{
  "actions": [...],
  "trace": [...],
  "scope": "rag",
  "scope_data": {
    "retrieved_context": [...],
    "public_rag_runtime": {...},
    "before_after_metrics": {...}
  }
}

---

Debugging

print(result.debug_summary())
print(result.to_dict())

Useful fields:

print(result.actions)
print(result.explanation)
print(result.trace)
print(result.scope_data)

---

Configuration

from aegis import AegisConfig

config = AegisConfig(
    mode="balanced",
    max_interventions=3,
    allow_retries=True,
    allow_retrieval_expansion=True,
    allow_context_reduction=True,
    allow_prompt_shaping=True,
    fallback="baseline",
    explain=False,
    emit_trace=False,
    policy=None,
    timeout_ms=30000,
)

---

Required Request Inputs

For scope calls, severity should be one of: low, medium, high.

Symptoms behavior:

• llm, rag, and step require explicit symptoms and severity
• context and agent provide safe defaults for symptoms and severity when omitted

Example:

result = client.auto().llm(
    base_prompt="You are a careful assistant.",
    symptoms=["inconsistent_outputs"],
    severity="medium",
)

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Design Principles

• runtime control over training
• minimal intervention
• observable behavior through trace and actions
• model-agnostic integration

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Documentation

Docs in /docs explain:

• architecture
• scopes
• Request Shapes
• result behavior
• integration guidance
• migration and usage patterns

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Examples

• OpenAI LLM Control
• RAG Pipeline
• Agent Workflow

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Status

• Stable SDK surface
• Active scopes: llm, rag, step, context, agent
• Public backend routes aligned to the scope-first contract

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License

MIT