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
or
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:
Agents propose. Governance decides. Missions continue.
Created for the All Things Agentic Hackathon.
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