Your AI Agent Shouldn’t Be Allowed to Write Whatever It Wants

Building a Write-Side Custody gate in Go

AI memory systems spend most of their design budget on retrieval. Which vector database? How should we chunk? Which embedding model? What should top_k be?

Those are useful questions, but they all arrive after something more consequential has already happened: the system decided that some piece of information deserved to become memory.

Consider an agent researching vendors for regulated workloads. It finds this statement:

Vendor X is approved for regulated workloads.

The source is Vendor X’s own marketing site.

The statement might be true. It might even be current. But the source does not have the authority to establish organizational security policy. If our agent writes it directly into durable memory, better retrieval will not save us. We have only made questionable evidence easier to find.

The problem is not storage. It is admission.

I’ve been calling the architectural boundary responsible for that decision Write-Side Custody. Let’s build a small one in Go.

What Write-Side Custody Does

A storage API answers a mechanical question:

Can I persist this object?

Write-Side Custody asks a different set:

  • Who is trying to write this?
  • Where did the information come from?
  • What authority is being claimed?
  • Is that source allowed to establish that authority?
  • Does policy permit this class of information to become durable?
  • What evidence should survive the decision?

Only after those are answered should storage become involved.

Flowchart showing a proposed write travelling from an agent or application into a Write-Side Custody gate. The gate routes accepted writes to durable memory and rejected writes to discard, while a dotted line records the decision in a Reasoning Ledger.

Note that the Reasoning Ledger does not make the decision. Custody enforces. The ledger witnesses. That separation matters a great deal once these systems have to be examined later.

Start With the Proposed Write

Go gives us a useful property for this experiment: we can make the things crossing our boundary explicit.

type ProposedWrite struct {
    Content          string
    Source           string
    ProducedBy       string
    ClaimedAuthority string
}

Our research agent might produce:

write := ProposedWrite{
    Content:          "Vendor X is approved for regulated workloads.",
    Source:           "https://vendorx.example.com/why-vendorx",
    ProducedBy:       "research-agent-run-4471",
    ClaimedAuthority: "security-policy",
}

Nothing in this structure says the statement is false, and that’s intentional. Write-Side Custody is not a universal truth detector. It determines whether a proposed write satisfies the rules governing this particular memory system.

For this system, a vendor marketing page cannot establish internal security policy. So we need policy.

Make Authority Explicit

First, two types. Authorities and source classes are different kinds of thing, and there is no situation in which we want to accidentally use one where the other belongs:

type Authority string
type SourceType string

Now we can define which source classes may establish which authorities:

type Policy struct {
    AuthoritySources map[Authority][]SourceType
}

var policy = Policy{
    AuthoritySources: map[Authority][]SourceType{
        "security-policy": {
            "internal-security-policy",
            "security-authority",
        },
        "user-preference": {
            "user",
        },
        "application-state": {
            "application",
            "runtime",
        },
    },
}

In production this comes from a policy service or configuration layer rather than a Go literal. The important part is that the relationship exists independently of whatever the agent claims. The agent does not get to decide that a marketing page constitutes security authority simply because it found one saying something useful.

Give the Gate a Verdict

type Verdict string

const (
    Allow Verdict = "ALLOW"
    Deny  Verdict = "DENY"
)

type CustodyDecision struct {
    Verdict   Verdict
    Reason    string
    Timestamp time.Time
}

Now the gate itself:

func EvaluateWrite(
    write ProposedWrite,
    sourceType SourceType,
    policy Policy,
) CustodyDecision {
    allowedSources, ok :=
        policy.AuthoritySources[Authority(write.ClaimedAuthority)]

    if !ok {
        return CustodyDecision{
            Verdict:   Deny,
            Reason:    "unknown claimed authority",
            Timestamp: time.Now().UTC(),
        }
    }

    for _, allowed := range allowedSources {
        if sourceType == allowed {
            return CustodyDecision{
                Verdict:   Allow,
                Reason:    "source may establish claimed authority",
                Timestamp: time.Now().UTC(),
            }
        }
    }

    return CustodyDecision{
        Verdict:   Deny,
        Reason:    "source cannot establish claimed authority",
        Timestamp: time.Now().UTC(),
    }
}

Two decisions in there are worth surfacing.

The first is that conversion on the map lookup. ProposedWrite holds plain strings because that’s what arrives over the wire, deserialized from JSON we did not write. Authority(write.ClaimedAuthority) is the moment an untrusted string becomes a term in our governance vocabulary, and it happens inside the gate rather than at the edge of the process. That’s the right place for it. Custody is precisely the layer where foreign input earns domain meaning.

The second is that sourceType is a separate parameter. It is not a field on ProposedWrite.

That is deliberate. Source classification is a judgment about the write, not a property the writer gets to assert about itself. If sourceType lived on the struct, our agent could label its own marketing page internal-security-policy and the gate would cheerfully agree. The classifier belongs to the custody layer, or to a runtime component that can independently observe where the content came from.

Small signature choice. Most of the security property.

Our vendor claim now reaches the boundary:

decision := EvaluateWrite(write, "vendor-marketing", policy)

fmt.Println(decision.Verdict)
fmt.Println(decision.Reason)

And receives:

DENY
source cannot establish claimed authority

The statement never becomes durable memory. We did not store questionable evidence and hope retrieval would eventually sort things out. We governed the write while the evidence and its provenance were still in hand.

The full gate, the policy, and a table-driven test suite covering the cases above are in memory-stack-patterns. Standard library only, so go test ./... and go run ./cmd/demo work on a clean checkout with nothing to install.

Don’t Throw Away the Rejection

Rejecting a write does not make the decision useless.

Imagine someone asks six months later:

Why doesn’t the system remember that Vendor X was approved?

“I don’t know” is not a satisfying answer, and in a regulated environment it isn’t an acceptable one either.

The custody decision is observable system behavior, which makes it a candidate for a Reasoning Ledger record:

type LedgerEntry struct {
    ID               string
    Timestamp        time.Time
    Actor            string
    Action           string
    Verdict          Verdict
    Reason           string
    Source           string
    ClaimedAuthority string
}

Our gate emits:

entry := LedgerEntry{
    ID:               newID(),
    Timestamp:        decision.Timestamp,
    Actor:            write.ProducedBy,
    Action:           "durable-memory-write",
    Verdict:          decision.Verdict,
    Reason:           decision.Reason,
    Source:           write.Source,
    ClaimedAuthority: write.ClaimedAuthority,
}

(newID is a few lines over crypto/rand, which keeps the whole example dependency-free.)

This entry is deliberately simplified. A ledger you would actually rely on needs a canonical serialization, a hash chain linking each entry to its predecessor, and some defense against tail truncation, because an append-only log that anyone can quietly shorten is not append-only.

Even the timestamp is less innocent than it looks. time.Now() gives you whatever precision the host clock offers, and JSON drops trailing zeros, so two entries can serialize at different widths. Hash a chain over a non-deterministic encoding and you have hashed nothing. The repo linked above truncates to a fixed precision and formats with a fixed-width layout for exactly that reason.

The Python implementation in the Sovereign Systems SDK does all three, which is part of why the Go exercise interests me. The hard parts are already solved somewhere. The open question is what happens to the boundary when it moves.

What matters here is the shape of what survives. The rejected statement still doesn’t enter memory. What persists is evidence that a write was proposed, evaluated, and rejected under a named rule. That is a different kind of information than the claim itself, and it’s the kind that answers questions later.

The Agent Doesn’t Get to Grade Its Own Homework

There’s a further boundary hiding in the payload. Suppose our agent sends:

{
  "content": "Vendor X is approved for regulated workloads.",
  "source": "https://vendorx.example.com/why-vendorx",
  "claimed_authority": "security-policy",
  "retrieval_method": "fresh",
  "policy_verified": true
}

Should we believe the last two fields?

There is an epistemic difference between:

The agent says it performed a fresh retrieval.

and:

The runtime that performed the HTTP request witnessed a fresh retrieval.

The same distinction applies to tool execution, timestamps, approval events, and policy versions. A stronger custody boundary therefore doesn’t only ask whether a record may be written. It asks:

Is this writer authorized to assert this particular kind of claim?

The agent legitimately owns claims about itself: its decision, the alternatives it considered, its confidence, the unknowns it identified. The runtime should mint the facts it can independently witness. Custody should not promote the former into the latter merely because both arrived in valid JSON.

This is the same principle as the sourceType parameter, applied one level up.

Why Do This at Write Time?

You could defer all of this to retrieval. Store everything, attach metadata, and let the reader decide what governs.

But then every questionable write becomes something every future reader has to reason around. It consumes storage. It becomes eligible for retrieval. It competes for context. It can be summarized, embedded, and propagated into records that no longer carry its provenance. And once provenance is gone, a future system may not have enough information to work out why the record was questionable in the first place.

A bad write today becomes bad context tomorrow.

Write-Side Custody puts the decision at the moment the system has the best possible view of what it is admitting.

Why Go?

None of this architecture requires Go, which is partly why I wanted to build it in Go.

A custody gate is a boundary service, and Go fits that role: explicit data structures, unremarkable HTTP services, small deployable binaries, and a type system strong enough to make the important distinctions visible without taking over the implementation.

The Authority and SourceType declarations we needed earlier are the clearest example. They cost one line each, and in exchange the compiler now refuses to let a source class be used where an authority belongs. That distinction would otherwise have lived in a variable name and a hope.

The same move applies elsewhere:

type Verdict string
type ActorType string

At which point the function signatures start expressing the vocabulary of the governance system rather than just its plumbing. EvaluateWrite doesn’t take three strings. It takes a proposed write, a source classification, and a policy, and no caller can shuffle them by accident.

Go didn’t create the architecture. It made the contracts hard to leave implicit.

Memory Begins Before Storage

Vector databases are very good at answering questions about similarity. They cannot tell us whether something deserved to become memory.

That’s an architecture decision, and by the time retrieval surfaces the problem, the questionable record may already have shaped dozens of others.

Give the proposed write provenance. Give the boundary policy. Give the decision evidence. Then let storage do what storage is good at.

Store what survived.


One thing I keep turning over: this boundary shouldn’t depend on Go. If Write-Side Custody only makes sense inside one language, it isn’t much of an architectural boundary. I’m curious what it would look like elsewhere. Would Rust’s type system make an invalid custody decision impossible to construct rather than merely inconvenient? Would Pydantic and FastAPI make the policy check feel so natural you’d stop noticing you were doing governance at all? If you’ve built something like this in your stack, I’d like to hear how the boundary changed shape.

Disclosure: I maintain the Sovereign Systems specification and SDK, which is where the vocabulary in this post comes from. The Go code here is a reference implementation written to test whether the idea travels, not a product.

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Beyond the Hype: Announcing the Open Source Sovereign Systems Specification & Pattern Library

We are currently building AI-native applications inside a linguistic and architectural vacuum.

Over the past year, the industry has thrown billions of dollars at frontier models and cloud orchestration tools while completely neglecting traditional data engineering discipline. We’ve been told that if we simply expand context windows to a million tokens and dump our raw, ambient conversational logs into a managed vector store, the LLM will magically sort it out at runtime.

It doesn’t. Instead, enterprises are hitting massive, systemic walls: attention fragmentation, positional bias (“Lost in the Middle”), data corruption, and skyrocketing API bills.

Recent architectural pivots across the industry—such as multi-agent frameworks shifting away from raw mesh networks to rigid supervisor trees—are symptoms of the exact same underlying disease: we are letting autonomous systems negotiate state through unstructured prose, burning compute without compounding capability.

To break through these walls, we don’t need larger context windows. We need structural boundaries.

Today, I am officially open-sourcing the Sovereign Systems Specification, Glossary, and Pattern Library to establish a rigid, defensive perimeter for local-first AI infrastructure.

Why Patterns Matter: From the Gang of Four to Local Silicon

When the software engineering industry faced the Wild West of early object-oriented development, the “Gang of Four” didn’t invent new languages; they formalized a shared vocabulary in Design Patterns: Elements of Reusable Object-Oriented Software. They gave us names for the invisible structures we were already struggling to build: Singletons, Adapters, Factories. Years later, when the industry shifted from relational tables to document stores, the MongoDB Design Patterns did the same thing for data architecture—formalizing paradigms like the Computed or Outlier patterns so developers could stop guessing how to handle polymorphic, non-relational scaling.

Patterns are essential because the laws of distributed systems do not change just because we throw a neural network in the middle. Right now, AI infrastructure lacks this formalized discipline. Developers are building highly volatile, cloud-dependent “digital attics” because they lack the structural primitives to build load-bearing context pipelines.

The Sovereign Systems Specification bridges this gap, providing repeatable, battle-tested architectural patterns for deterministic, cost-aware, and high-integrity AI inference.

The Sovereign Architecture: Three Pillars of State Control

The core thesis of this resource is simple: We must shift from query-time reasoning to strict write-time ingestion boundaries. We treat incoming payloads as untrusted telemetry on local silicon before an external orchestrator ever touches a cloud model.

This open-source release is split into three distinct, load-bearing resources:

  1. The Sovereign Systems Glossary
    A formalized dictionary designed to give engineering teams a shared vocabulary for data flow, risk, and state control. It moves past prompt-engineering “magic spells” and defines rigid terms like:
    • The Prose Tax & Context Inflation Tax: The geometric compounding of financial cost and model attention decay that occurs when you pass un-optimized, raw text streams across the network.
    • Write-Side Custody: The architectural discipline of enforcing structural validation, cryptographic signing, and metadata parsing at the exact point of ingestion before data ever commits to long-term memory.
    • The Digital Attic (Anti-Pattern): The chaotic enterprise trap of dumping unvetted, unstructured raw logs into vector storage and assuming semantic search can reliably reconstruct operational context at runtime.
  2. The Architecture & Execution Framework (/ARCHITECTURE)
    Comprehensive visual blueprints, execution pipeline flows, and runtime orchestration layouts. These documents map the exact physical transition from cloud-dependent, API-mediated routing to localized, edge-native context processing—ensuring data custody and reasoning models remain entirely unified within a secure local boundary.

  3. The Sovereign Inference Pattern Library (/PATTERNS)
    Repeatable, low-level structural primitives for context engineering. It includes detailed layouts for patterns like the Sieve-and-Sign Pattern (aggressively filtering input for semantic noise locally and stamping it with a cryptographic signature) and Pre-Paid Retrieval Precision (paying a fixed token cost upfront to structure context, eliminating the compounding cost of positional bias during runtime queries).

Accessing the Resources

The entire specification index, architectural layouts, and pattern files are open, human-readable, and live today on GitHub Pages:

How to Contribute

This is a living framework built for practitioners who are actively wrestling with these constraints in production. We are explicitly looking for community contributions to expand this shared language:

  • Pattern Submissions: Have you engineered a repeatable runtime or filtering primitive that successfully prevents boundary deflection or context inflation? Submit an architectural RFC.
  • Case Studies & Anti-Patterns: If your team has successfully migrated away from an ambient context loop or survived a “digital attic” metadata collapse, your post-mortem belongs in this index.
  • Documentation Refinements: Help us sharpen definitions, expand the visual data flow blueprints, or map these patterns to specific local Small Language Model (SLM) topologies.

Check out the specification repo, star the project, and open an issue or pull request to get involved:

Sovereign Systems Specification on GitHub

Let’s stop building fragile cloud wrappers. Let’s start engineering sovereign systems.

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