UNWIND: Building Governed Autonomous Operations for AI Agents

Akash Bichukale — Sun, 30 Aug 2026 20:07:14 +0000

UNWIND — Governed Autonomous Operations

Autonomous AI agents can plan, delegate, execute, and adapt. But as agents become more capable, the cost of an incorrect autonomous action also increases.

So I built UNWIND — Governed Autonomous Operations around a different question:

How do we make autonomous agents capable of acting while keeping their actions bounded, explainable, evidence-backed, and governable?

UNWIND treats an autonomous workflow as a mission rather than a simple agent loop.

The system follows:

Objective → Plan → Delegate → Execute → Verify → Reconcile → Govern → Act → Remember → Adapt

The key principle is simple:

Agents propose. Governance decides. Missions continue.

Built for the All Things Agentic Hackathon

I created this project-build walkthrough for the All Things Agentic Hackathon, documenting how I built UNWIND — Governed Autonomous Operations.

The project explores governed autonomy through:

bounded specialist agents
deterministic execution boundaries
output contracts
evidence reconciliation
human authorization
immutable mission checkpoints
cross-mission memory
self-correction through rejection and replanning

The Architecture

UNWIND is organized into five major layers.

Layer 1 — Foundation

The foundation provides the structures required for controlled execution:

Output contracts
Evidence store
Knowledge store
Time machine / checkpoints
Provenance and audit information

The goal is to make important decisions traceable instead of treating the final model response as an isolated answer.

Layer 2 — Specialist Fleet

The orchestrator delegates work to scoped specialists.

The prototype includes:

Recon
Risk
Reconciler
Remediation
Verifier

Each specialist operates within a defined responsibility rather than receiving unrestricted authority.

Layer 3 — Execution & Contracts

Agent outputs do not automatically become actions.

They first pass through an Output Contract Check that validates:

schema
safety
policy
provenance

If an output fails the contract, the mission can follow the self-correction path:

REJECTED → REPLAN

This creates a deterministic boundary around agent execution.

Layer 4 — Governance & Memory

This is the core of UNWIND.

Evidence from different sources can disagree.

Instead of silently selecting one answer, UNWIND evaluates evidence using factors such as authority and recency, while preserving disagreement as an explicit state.

For example:

RESOLVED

RESOLVED_WITH_DISPUTES

This makes uncertainty visible instead of hiding it.

Human Governance Gate

Consequential actions should not happen simply because an autonomous agent proposed them.

UNWIND therefore places a mandatory human gate before external actions.

The execution path becomes:

Delegate → Contract Check → Reconcile → Human Gate → External Action

The human gate provides an explicit authorization boundary.

This creates a distinction between:

What an agent can do

and

What an agent is authorized to do.

Evidence First

UNWIND is designed around an evidence-first approach.

Important mission decisions can be associated with:

claims
sources
decisions
artifacts
checkpoints
provenance

This makes the mission observable and allows the system to preserve the context behind important decisions.

Mission Time Machine

A final answer is not enough to understand an autonomous system.

UNWIND maintains mission checkpoints so execution can be inspected as a sequence of states.

A mission can progress through:

OBJECTIVE → PLAN → DELEGATE → EXECUTE → RECONCILE → GOVERN → ACTION → DISTILL → NEXT MISSION

The checkpoint model supports inspection, replay, and recovery-oriented workflows.

Cross-Mission Memory

A new mission should not necessarily start from zero.

UNWIND can distill useful information from one mission into structured knowledge and make that knowledge available to a future mission.

The loop is:

MISSION N → DISTILL → KNOWLEDGE → RECALL → MISSION N+1

This is intended to preserve useful lessons, experiences, patterns, and mission knowledge across executions.

Inside the Control Tower

The operational interface exposes the state of the autonomous system instead of hiding everything behind a chat window.

It provides visibility into:

mission execution
governance layers
agent fleet
reconciliation
warrants / capability boundaries
knowledge recall
checkpoints
external actions
evidence integrity

The goal is to make autonomy observable and accountable.

Self-Correcting Execution

Autonomous systems can produce invalid or conflicting results.

UNWIND treats this as a state the system must handle explicitly.

The control loop is:

PLAN → DELEGATE → EXECUTE → CONTRACT CHECK → RECONCILE → GOVERN → ACTION

If the result is rejected:

REJECTED → REPLAN → EXECUTE AGAIN

Instead of silently accepting an invalid result, the mission can return to planning and correct its course.

Google Cloud & Google AI

UNWIND uses Google Cloud and Google agent/model tooling as part of the implementation.

The project uses:

Google Agent Development Kit (ADK)
Google GenAI SDK
Cloud Run
Firestore
Pub/Sub
Gemini
Veo
Lyria

The architecture keeps orchestration, governance, evidence, memory, and deployment as explicit system components.

Mission Media

UNWIND also connects mission intelligence with generated media.

The mission interface includes:

Gemini for mission intelligence
Veo for visual replay
Lyria for audio signals

These outputs can be associated with the mission as observable artifacts.

Why UNWIND?

The goal was not simply:

“How autonomous can I make an agent?”

The more important question was:

“How much autonomy can I make governable?”

That changes the architecture.

Instead of optimizing only for agent freedom, UNWIND focuses on:

bounded autonomy
explicit authorization
evidence-backed decisions
visible disagreement
human oversight
reproducibility
cross-mission learning

Design Principles

1. Honest by Design

No silent certainty when evidence conflicts.

2. Governed Autonomy

Agents operate within explicit boundaries.

3. Evidence First

Important decisions remain traceable to evidence.

4. Self-Correcting

Rejected results can return to replanning.

5. Continuous Memory

Useful knowledge can flow from one mission into the next.

What I Built

UNWIND turns the conventional:

Agent → Tool → Answer

into:

Agents → Evidence → Contracts → Reconciliation → Governance → Action → Memory

The objective is not to create an agent that can never fail.

The objective is to make autonomous failure:

visible, bounded, recoverable, and governable.

That is the core idea behind UNWIND.

Autonomy should not mean absence of control.

It should mean the ability to operate within clearly defined control boundaries.

Demo

The working UNWIND system demonstrates mission execution, governance, evidence reconciliation, human authorization, checkpoints, and cross-mission memory.

Project Demo:

https://youtu.be/Mxi7vc7OXPk

Final Thoughts

As AI agents become more capable, the architecture surrounding them becomes as important as the model itself.

Planning, delegation, execution, evidence, authorization, memory, and recovery need to work together.

UNWIND explores that direction: