For CTOs and architects: the missing layer in your AI stack.
Why This Is Different
Today's AI stack has a missing layer. Orchestrators coordinate agents, but nothing governs them. DaemonCore fills this gap.
The Current Stack
Most AI systems follow this pattern:
The gap between orchestration and models is typically bridged with prompt engineering — advisory instructions that agents should follow but aren't required to.
What Breaks
Without a governance layer:
Boundaries Are Suggestions
You tell an agent "don't access external APIs" via prompt. But there's nothing enforcing this. A clever prompt injection or edge case can bypass it.
Multi-Agent Chaos
When multiple agents work together, who decides permissions? The orchestrator can grant capabilities, but nothing prevents one agent from exceeding its scope.
Vendor Lock-In
Safety rules get baked into prompts specific to each model. Switching providers means rewriting everything. Governance isn't portable.
Audit Impossibility
What did the agent actually do? Without structural boundaries, you can't verify that an agent stayed within its lane. Compliance becomes guesswork.
Why Governance Must Be Beneath Orchestration
Some orchestrators include "safety features." Why isn't that enough?
Because governance at the orchestration layer can be bypassed by the orchestrator. If the orchestrator decides to ignore a rule, there's nothing below it to enforce compliance.
Consider traditional operating systems: applications don't enforce their own memory boundaries. The OS does. Applications can't decide to access memory they shouldn't have — the enforcement happens at a lower level.
DaemonCore applies this principle to AI systems. Governance sits beneath orchestration so that orchestrators operate within defined constraints they cannot override.
What This Enables
Trustworthy Agents
When boundaries are architectural, you don't hope agents behave — you know they will. Safety becomes a property of the system, not a prayer.
Safe Scaling
Add more agents without expanding your risk surface. Each agent inherits governance from the layer below. More capability doesn't mean more vulnerability.
Portable Safety
Switch from Claude to GPT to Gemini without rewriting safety rules. Governance is defined once and enforced regardless of which model runs beneath it.
Real Auditability
Every agent action traces to a defined capability. Compliance isn't "we told the model not to" — it's "the system prevented it."
The Aha Moment
Here's the realisation that matters:
AI systems are being deployed at scale without the governance layer that every other computing environment takes for granted. We're building multi-agent systems on hope and prompt engineering.
DaemonCore is that missing layer. Not another orchestrator. Not another framework. The foundation that makes orchestrators and frameworks trustworthy.
How DaemonCore Enforces This
Action Protocol
Agent outputs must match a defined protocol shape. Validation happens before execution—not after.
MAX Messaging Bus
Inter-agent messages are typed and schema-validated. Malformed messages are rejected at the routing layer.
Template Operations
Complex tasks follow predefined templates that specify scope, required checks, and output format.
V1 vs V1.5
V1 (Current)
Protocol validation, schema enforcement, template constraints, audit logging. Defence through structure.
V1.5 (Planned)
Cryptographic signing, hardware attestation, real-time verification. Defence through cryptographic proof.
In Practice
Same models. Same tools. Same goals. Different environment.
Multi-Agent Coordination
Without DaemonCore
- Each agent operates with its own interpretation of boundaries, shaped by prompts that may be read differently by different models
- When agents collaborate, coordination depends on application-level conventions that may not be consistently followed
- Expanding the number of agents increases the surface area for unexpected interactions
- Teams spend effort validating that agents stayed within their intended scope after the fact
- Permission boundaries exist as guidance that agents are asked to respect
With DaemonCore
- Agents operate within defined capability boundaries at a layer beneath the application
- Coordination protocols remain consistent regardless of which models are involved
- Adding agents does not proportionally increase governance complexity
- Boundaries are properties of the environment, not instructions subject to interpretation
- Audit records reflect what agents were permitted to do, not just what they were told
Long-Running Workflows
Without DaemonCore
- Session state is typically managed within prompts or application memory
- When workflows span multiple sessions, context reconstruction depends on how well the application can re-establish prior state
- Agents may behave differently after restarts, depending on how context is reintroduced
- Continuity relies on careful prompt design and state serialisation
- Investigating behavioural drift requires reviewing prompt sequences and application logs
With DaemonCore
- Session state is maintained independently of individual model sessions
- Workflows can resume with consistent context across restarts
- Agent behaviour remains stable when operating under the same configuration
- State is explicit and inspectable at defined points
- Behavioural changes can be traced to configuration or context changes rather than prompt variation
Model Migration
Without DaemonCore
- Safety rules and behavioural constraints are often embedded in prompts tailored to specific models
- Switching providers typically requires adapting prompt strategies
- Different models may interpret the same instructions with varying results
- Migration testing focuses on whether the new model follows existing prompt patterns
- Safety posture may shift when models are changed
With DaemonCore
- Governance rules are defined independently of which model runs beneath them
- Model changes occur within the same boundary definitions
- Migration testing focuses on capability verification rather than prompt revalidation
- Safety posture remains stable across provider changes
- Teams can evaluate new models without restructuring their governance approach