Metadata-Version: 2.5
Name: agentic-sidecar
Version: 0.0.1
Summary: Companion intelligence and real-time decision supervision for autonomous AI agents.
Project-URL: Homepage, https://github.com/DeepAgentLabs/agentic-sidecar
Project-URL: Repository, https://github.com/DeepAgentLabs/agentic-sidecar
Project-URL: Issues, https://github.com/DeepAgentLabs/agentic-sidecar/issues
Project-URL: Changelog, https://github.com/DeepAgentLabs/agentic-sidecar/blob/main/CHANGELOG.md
Author: agentic-sidecar Contributors
License: MIT License
        
        Copyright (c) 2026 pramodbn27
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: agents,decision-supervision,governance,intent,llm,safety
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Requires-Dist: pydantic<3,>=2.0
Provides-Extra: agentic-chaos
Requires-Dist: agentic-chaos>=0.1; extra == 'agentic-chaos'
Provides-Extra: agenticlens
Requires-Dist: agenticlens>=0.1.3; extra == 'agenticlens'
Provides-Extra: dev
Requires-Dist: build>=1.2; extra == 'dev'
Requires-Dist: mypy>=1.10; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.6; extra == 'dev'
Requires-Dist: twine>=5.1; extra == 'dev'
Description-Content-Type: text/markdown

# agentic-sidecar

**A companion intelligence and real-time supervision layer for autonomous AI agents.**

> The Main Agent acts. The Sidecar observes, thinks, advises, and governs.

## Status

**Concept / pre-implementation.** This repository currently contains the
architecture proposal ([`concept.md`](concept.md)) and this README
— no package code, no PyPI release, no CI yet. See
[ROADMAP.md](ROADMAP.md) for the build plan, starting with a narrow,
LLM-free v0.1.

## Contents

- [Why](#why)
- [What it is not](#what-it-is-not)
- [Architecture](#architecture)
- [Sidecar vs. Agent Harness](#sidecar-vs-agent-harness)
- [Main Agent vs. Sidecar](#main-agent-vs-sidecar)
- [How the Decision Gate evaluates a decision](#how-the-decision-gate-evaluates-a-decision)
- [Sidecar modules](#sidecar-modules)
- [Planned Python API](#planned-python-api)
- [Operating modes](#operating-modes)
- [Long-term: an intent propagation layer](#long-term-an-intent-propagation-layer)
- [The DeepAgentLabs ecosystem](#the-deepagentlabs-ecosystem)
- [Roadmap](#roadmap)
- [License](#license)

## Why

Autonomous agents chain a lot of reasoning and tool calls between a user's
request and a real-world effect:

```text
User → Agent → Planning → Sub-Agent → MCP → Tool → API → External System
```

Three things go wrong in that chain that permission checks alone don't catch:

- **Intent drift** — "investigate why production is slow" quietly becomes
  "restart the production database." Nobody revoked authority; the agent's
  plan just wandered.
- **Technically-allowed, contextually-wrong actions** — `refund_customer()`
  is a permitted tool call. Refunding $850 against a $500 authorization is
  not a permitted *decision*, even though the tool call succeeds.
- **No live visibility** — long-running agents act for minutes at a time
  with no answer to "what is it doing right now, and can I stop it?"

`agentic-sidecar` is designed to attach to any agent framework, but the first
release proves that against one framework only — LangGraph — before claiming
the rest (see [Design Constraints](ROADMAP.md#design-constraints-read-before-building-v01)
in ROADMAP.md). Either way, it answers a different question than permission
checks or observability tools do:

> **Not** "can this agent call this tool?" — **"should it, right now, given
> what the user actually asked for?"**

## What it is not

- **Not a multi-agent framework.** The Sidecar never owns or executes the
  task plan, and it never replaces a worker agent. It may independently
  critique the Main Agent's plan or propose alternatives (the Planner
  module) — but the Main Agent decides whether to act on that critique, and
  remains the one that executes.
- **Not a content-safety guardrail.** Libraries like NeMo Guardrails or
  Guardrails AI validate a single prompt/response turn against rules. The
  Sidecar evaluates a *decision* — a tool call or plan step — against intent
  and context accumulated across an entire task.
- **Not a tracing/observability tool.** That's
  [AgenticLens](https://github.com/DeepAgentLabs/agenticlens)'s job:
  "what did the agent do, and why did it fail?" — after the fact. The
  Sidecar's job is "should it continue?" — while it's still running.
- **Not just LLM-as-a-Judge.** A judge model is one possible evaluator inside
  one Sidecar module (§ [Sidecar modules](#sidecar-modules)). Most decisions
  should never reach an LLM at all — see [Operating
  modes](#operating-modes) and the cost-control design in
  [ROADMAP.md](ROADMAP.md).
- **Not an agent harness.** It doesn't run the agent loop, manage tools, or
  handle retries — that's LangGraph's, CrewAI's, or your custom loop's job.
  The Sidecar attaches to whatever harness is already running the agent; see
  [Sidecar vs. Agent Harness](#sidecar-vs-agent-harness).

## Architecture

```text
                    USER
                      │
                 User Intent
                      │
                      ▼
              ┌───────────────┐
              │  MAIN AGENT   │
              │   EXECUTOR    │
              └───────┬───────┘
                      │
              Plans / Decisions
                      │
                      ▼
            ┌─────────────────────┐
            │   AGENTIC SIDECAR   │
            │                     │
            │ Intent Guardian     │
            │ Planner             │
            │ Critic              │
            │ Judge               │
            │ Risk Evaluator      │
            │ Policy Advisor      │
            │ Decision Gate       │
            │ Status Interpreter  │
            └──────────┬──────────┘
                       │
          Advice / Approval / Challenge
                       │
                       ▼
                 MAIN AGENT
                       │
                       ▼
                  MCP / Tools
                       │
                       ▼
                External Systems
```

The Sidecar is attached to the execution lifecycle. It does not own the
user's task and is never a second worker in the plan.

## Sidecar vs. Agent Harness

An **agent harness** (LangGraph, a custom loop, OpenAI Agents SDK, Microsoft
Agent Framework, ...) is the control and execution environment: it runs the
agent loop and manages tools, state, context, retries, and lifecycle.
`agentic-sidecar` doesn't replace that — it attaches to it as a decision-time
supervision layer:

```text
Agent Harness   = control and execution loop.
Agentic Sidecar = decision-time supervision layer for that loop.
```

The harness answers *"how do I execute this workflow?"* The Sidecar answers
*"should this decision happen, given what the human originally asked for?"*

This distinction matters most once a task fans out across a delegation
chain — Agent A → Agent B → Agent C → MCP/Tool — where each hop tends to
receive only the sub-task it needs to perform, not the original constraints
and authority behind it:

```text
Human Intent → Agent A → delegates → Agent B → delegates → Agent C → MCP/Tool
```

The Sidecar's Intent Envelope (§ [Sidecar modules](#sidecar-modules)) is
designed to travel with the task through that chain instead of being
reconstructed from conversation history at every hop — see [Long-term: an
intent propagation layer](#long-term-an-intent-propagation-layer).

Where it pays for itself: without a supervision layer, a harness typically
discovers a bad decision only after acting on it —

```text
Plan → Act → Fail → Recover → Replan
```

— versus catching it before execution:

```text
Plan → Sidecar Check → Act
          ├── ALLOW
          ├── CHALLENGE
          ├── REPLAN
          ├── BLOCK
          └── ESCALATE
```

That's primarily a reliability and control win, not a speed win — the
Sidecar doesn't make the underlying model faster, it reduces unnecessary
actions, unsafe retries, and repeated context reconstruction. Full treatment:
[concept.md § 23](concept.md#23-sidecar-vs-agent-harness).

## Main Agent vs. Sidecar

| Main Agent | Agentic Sidecar |
| --- | --- |
| Accomplishes the task | Maintains original intent |
| Reasons about the domain problem | Independently evaluates plans and decisions |
| Selects and calls tools | Challenges questionable decisions |
| Interacts with MCP servers / APIs | Evaluates risk and checks policy |
| Executes actions | Decides when human approval is required |
| Produces the final result | Explains live execution; recommends replanning; pauses or blocks when configured |

## How the Decision Gate evaluates a decision

Three modules ask three genuinely different questions about the same
proposed action, and the roadmap's job is to keep them from collapsing into
one generic "policy engine":

```text
             USER INTENT
                  │
          Intent Envelope
                  │
                  ▼
             MAIN AGENT
                  │
           proposed action
                  │
                  ▼
              SIDECAR
          ┌───────┼────────┐
          │       │        │
       Policy    Risk    Intent
          │       │        │
   "Are you    "How      "Is this
   permitted   dangerous  actually what
   to do       is this    the human
   this?"      action?"   asked you to
                          accomplish?"
          │       │        │
          └───────┼────────┘
                  │
             DECISION GATE
                  │
       ┌──────────┼──────────┐
       ▼          ▼          ▼
     ALLOW      REPLAN     ESCALATE
                              │
                            HUMAN
```

Policy and Risk are largely mechanical — permission lists, thresholds,
tool-argument patterns — and v0.1 ships them with zero LLM calls (see
[ROADMAP.md](ROADMAP.md)). Intent is the one that requires understanding
what the user actually meant, and it's where the project's distinctive
value lives.

The diagram above shows the target outcome set. **v0.1 ships a narrower
slice**: only `ALLOW`/`BLOCK`, in Observe mode — the Sidecar logs what it
*would* have decided but cannot yet stop an action in practice. `WARN`,
`CHALLENGE`, `REPLAN`, `PAUSE`, and `ESCALATE` arrive across v0.2–v0.4 as
Intent Guardian and the full Decision Gate ship. Version-by-version build
order is in [ROADMAP.md](ROADMAP.md#build-order).

## Sidecar modules

Users enable only what they need:

```text
Agentic Sidecar
│
├── Intent Guardian     — Intent Envelope, alignment checks, drift detection
├── Planner              — independently evaluates the agent's plan
├── Critic                — challenges a proposed decision before it executes
├── Judge                 — optional independent (and independent-model) evaluation
├── Risk Evaluator        — classifies an action's risk before deciding whether to escalate
├── Policy Advisor        — deterministic policy rules (cheapest check, runs first)
├── Decision Gate         — turns evaluations into ALLOW / WARN / CHALLENGE / REPLAN / PAUSE / BLOCK / ESCALATE
├── Budget Guardian       — cost/token ceilings per task
├── Status Interpreter    — translates raw tool/MCP traces into human-readable narration
└── Human Escalation      — pauses execution and requests approval
```

```yaml
sidecar:
  intent:
    enabled: true
    preserve_original_intent: true
  planner:
    enabled: false        # off by default — see cost design in ROADMAP.md
  critic:
    enabled: false        # off by default — see cost design in ROADMAP.md
  policy:
    enabled: true
    source: policies.yaml
  risk:
    enabled: true
    intervention_threshold: 0.80
  judge:
    enabled: false        # off by default — see cost design in ROADMAP.md
    model: independent-model
  budget:
    enabled: true
    max_cost: 2.00
  human_approval:
    enabled: true
  on_sidecar_failure: fail_closed   # or fail_open — see ROADMAP.md
```

## Planned Python API

This is the target developer experience — **not yet implemented** (tracked
as v0.1 in [ROADMAP.md](ROADMAP.md)):

```python
from agentic_sidecar import Sidecar

sidecar = Sidecar(roles=["intent_guardian", "policy", "risk", "planner", "critic"])
agent = sidecar.attach(my_agent)

agent.run("Investigate the production issue but do not modify production.")
```

```python
@sidecar.before_tool_call
def evaluate_action(context):
    return sidecar.evaluate(
        intent=context.intent,
        action=context.tool_call,
        risk=context.risk,
    )
```

```python
Decision(
    status="REPLAN",
    risk="HIGH",
    reason="Action exceeds original user intent",
)
```

## Operating modes

| Mode | Behavior |
| --- | --- |
| **Observe** | Sidecar monitors and logs; cannot affect execution. |
| **Advise** | Sidecar returns a recommendation; the agent decides whether to follow it. |
| **Govern** | Sidecar's Decision Gate can allow, warn, replan, pause, or block. |
| **Human-supervised** | High-risk decisions route to a human for approve/reject. |

Modes are meant to be adopted in that order — organizations start in Observe
and move to Govern once they trust the signal.

## Long-term: an intent propagation layer

The nearer-term modules above are the whole of what v0.1–v0.8 ship. But the
`IntentEnvelope` (§ [Sidecar modules](#sidecar-modules)) is designed to
survive being handed off — not just checked once and discarded:

```text
Human
  │
  └── Intent Envelope #182
            │
            ▼
         Agent A
            │
        delegates
            ▼
         Agent B
            │
           MCP
            ▼
         Agent C
            │
           Tool
```

If every hop in a delegation chain can answer *what was originally
requested, who authorized it, what constraints apply, what authority was
actually delegated, and whether intent has since changed* — that's no
longer just a feature of one package. It's closer to an interoperability
concern for autonomous systems generally, which is why v1.0 targets
publishing the envelope as a versioned schema in `ai-operations-spec`
rather than keeping it as an internal Sidecar structure (see the v1.0 entry
in [ROADMAP.md](ROADMAP.md#build-order)).
This is explicitly a v1.0 target, not something v0.1 needs to anticipate —
noted here because it's the reason the envelope's shape deserves care early,
even though nothing consumes it across process boundaries yet.

## The DeepAgentLabs ecosystem

```text
                 DeepAgentLabs
              Autonomous AI Systems
                      │
        ┌─────────────┼─────────────┐
        │             │             │
     OBSERVE         GUIDE        CONNECT
        │             │             │
 AgenticLens    Agentic Sidecar  Agentic MCP
        │             │             │
        └─────────────┼─────────────┘
                      │
                     TEST
                      │
                Agentic Chaos
```

| Project | Question it answers |
| --- | --- |
| [AgenticLens](https://github.com/DeepAgentLabs/agenticlens) | What did the agent do, and what happened? |
| [Agentic Chaos](https://github.com/DeepAgentLabs/agentic-chaos) | How does the agent behave when things go wrong? |
| [Agentic MCP](https://github.com/DeepAgentLabs/mcp-server) | How does the agent interact with tools and external capabilities? |
| **Agentic Sidecar** | Should the agent continue with this decision, and is it still acting according to intent? |

Each project is independently installable; none requires another as a hard
dependency (see [Cross-Project Dependencies](ROADMAP.md#cross-project-dependencies)
in the roadmap).

## Roadmap

Full build plan, version sequencing, and design constraints:
[ROADMAP.md](ROADMAP.md).

Original architecture proposal: [concept.md](concept.md).

## License

MIT (planned — `LICENSE` file to be added alongside the first code commit,
matching sibling DeepAgentLabs projects).
