Aegis-Forge is a private self-hosted AI engineering control-plane project focused on giving an operator controlled visibility over multi-agent AI-assisted development workflows.
The project explores how to run AI agents under explicit policy, governance, observability, and human approval boundaries.
This public note describes the architecture intent and operational principles only. It does not expose source code, endpoints, internal topology, credentials, private prompts, implementation details, or product roadmap internals.
AI agent execution becomes risky when model calls, tool usage, approvals, audit trails, and runtime behavior are spread across disconnected tools.
Common failure modes include:
- invisible tool execution
- unclear provenance
- uncontrolled cost growth
- weak approval boundaries
- missing runtime health visibility
- no consistent policy enforcement
- unreviewed autonomous changes
- poor auditability across multi-agent runs
Design a private control-plane that makes AI-assisted engineering workflows:
- observable
- policy-aware
- operator-controlled
- self-hosted
- auditable
- reversible where possible
- safe for staged adoption
Operator UI
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Control Plane
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+--> Run Queue
+--> Agent Profiles
+--> Model Gateway
+--> Tool Runtime Layer
+--> Knowledge Plane
+--> Policy Engine
+--> Audit Log
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Human Approval / Rejection Boundary
The operator should be able to inspect:
- active runs
- pending approvals
- failed runs
- denied tool calls
- model/provider used
- stage-level provenance
- cost and usage signals
- runtime health
The model gateway abstracts access to multiple model providers and local runtimes.
Public documentation intentionally avoids exact provider routing rules, credentials, usage data, and private budget policy details.
Agent profiles separate responsibilities such as:
- planning
- implementation
- review
- operations
- documentation
The goal is not full autonomy. The goal is controlled delegation with visible review boundaries.
Tool runtimes should be treated as operational dependencies, not invisible extensions.
Relevant concerns:
- installation state
- version pinning
- auth readiness
- rollback path
- disabled/degraded states
- operator-approved execution
Tool calls should be evaluated against explicit policies before execution.
Policy examples:
- allowed working directory scope
- allowed tool categories
- blocked destructive actions
- credential-related action gates
- production-change gates
- deny-over-allow precedence
A private knowledge layer can reduce repeated context loading and improve consistency.
The public note intentionally avoids internal schemas, indexes, private documents, or retrieval implementation details.
A useful control-plane should expose:
- run ID
- workspace
- initiating user/operator
- selected agent profile
- selected model/provider
- tools requested
- tools allowed or denied
- approval decisions
- retry count
- failure reason
- estimated usage/cost
- final output state
The project assumes that agentic execution should be staged:
Read-only research
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Draft-only generation
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Workspace-limited edits
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Pull request generation
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Human-reviewed merge
Agent merge authority is intentionally excluded from the safe default model.
This public note does not include:
- source code
- real endpoints
- internal service names
- private deployment configuration
- exact database schema
- exact model routing rules
- private policy engine code
- budget ledger details
- production credentials
- roadmap implementation details
Private R&D. Public documentation is limited to sanitized architecture notes, risk framing, and operating principles.