AgentMesh Architecture

Trust-first communication layer for AI agents — cryptographic identity, multi-dimensional trust scoring, scope chains, and governance enforcement.

1. Overview

AgentMesh provides a 4-layer trust stack that lets autonomous AI agents discover, authenticate, and collaborate with each other while maintaining cryptographic accountability and policy compliance at every hop.

graph TB
    subgraph AgentMesh
        L4[Layer 4 — Reward]
        L3[Layer 3 — Governance]
        L2[Layer 2 — Trust]
        L1[Layer 1 — Identity]
    end

    External["External Agents<br/>(A2A · MCP · IATP · ACP)"] -->|TrustBridge| L2
    L1 -->|DID + Ed25519| L2
    L2 -->|Trust Score| L3
    L3 -->|Policy Verdict| L4
    L4 -->|Reputation Signal| L2

    Storage[(Storage<br/>Redis · Postgres · Memory)] --- L1
    Storage --- L2
    Storage --- L3
    Storage --- L4

Module Map

src/agentmesh/
├── identity/        # Layer 1 — DID, credentials, delegation, SPIFFE
├── trust/           # Layer 2 — scoring, handshake, bridge, capability
├── governance/      # Layer 3 — policy, OPA, audit, compliance, shadow
├── reward/          # Layer 4 — engine, scoring, trust decay, anomaly
├── integrations/    # Protocol adapters (A2A, MCP, LangFlow, …)
├── storage/         # Pluggable backend (Redis, Postgres, memory)
├── services/        # Backend services
├── cli/             # CLI commands
├── core/            # Shared core types
└── observability/   # Metrics and tracing

2. 4-Layer Trust Stack

graph LR
    subgraph "Layer 1 — Identity"
        DID["DID<br/>did:mesh:hex"]
        Ed["Ed25519<br/>Key Pair"]
        SPIFFE["SPIFFE SVID<br/>X.509 / JWT"]
        AICard["AI Card<br/>Signed Discovery"]
    end

    subgraph "Layer 2 — Trust"
        TS["Trust Score<br/>0–1000"]
        HS["Handshake<br/>Challenge-Response"]
        TB["TrustBridge<br/>A2A · MCP · IATP · ACP"]
        Cap["Capability<br/>action:resource:qualifier"]
    end

    subgraph "Layer 3 — Governance"
        Pol["Policy Engine<br/>YAML/JSON + Rego"]
        OPA["OPA Integration"]
        Audit["Hash Chain Audit<br/>Chain"]
        Shadow["Shadow Mode"]
    end

    subgraph "Layer 4 — Reward"
        Rep["Reputation<br/>Engine"]
        Decay["Trust Decay<br/>Engine"]
        Anomaly["Anomaly<br/>Detection"]
    end

    DID --> HS
    Ed --> HS
    SPIFFE --> HS
    AICard --> TB
    HS --> TS
    TS --> Pol
    Pol --> OPA
    Pol --> Audit
    Shadow -.->|dry-run| Pol
    TS --> Rep
    Rep --> Decay
    Decay --> Anomaly
    Anomaly -->|feedback| TS

Layer Details

Layer	Responsibility	Key Modules
1 — Identity	Ed25519 key pairs, DID generation (`did:mesh:<hex>`), SPIFFE mTLS, AI Card discovery, scope chains	`identity/`
2 — Trust	5-dimension trust scoring (0–1000), 3-phase handshake (<200 ms), TrustBridge protocol unification, capability grants	`trust/`
3 — Governance	Declarative policy rules (<5 ms), OPA/Rego integration, hash chain audit trails, compliance mapping (EU AI Act, SOC 2, HIPAA, GDPR), shadow mode	`governance/`
4 — Reward	Reputation engine, weighted scoring, trust decay (2 pts/hr), anomaly detection (5 classes)	`reward/`

3. Identity Lifecycle

sequenceDiagram
    participant Agent
    participant Identity as identity/agent_id
    participant Cred as identity/credentials
    participant SPIFFE as identity/spiffe
    participant Trust as trust/handshake
    participant Cap as trust/capability

    Agent->>Identity: generate DID<br/>SHA256(name:org:uuid) → did:mesh:hex
    Identity->>SPIFFE: register SVID<br/>(X.509, 1h TTL)
    Identity->>Cred: issue credential<br/>(scoped capabilities, 15 min TTL)
    Agent->>Trust: initiate handshake
    Trust->>Trust: Phase 1 — challenge (nonce, 30s expiry)
    Trust->>Trust: Phase 2 — peer signs nonce (Ed25519)
    Trust->>Trust: Phase 3 — verify signature + trust score
    Trust->>Cap: grant capabilities<br/>(action:resource:qualifier)

    loop Every < 10 min remaining
        Cred->>Cred: zero-downtime rotation
    end

    Note over Cred: Revocation propagation ≤ 5 s
    Agent->>Cred: revoke credential
    Cred-->>Trust: broadcast revocation

Key Parameters

Parameter	Value	Source
DID format	`did:mesh:<32-char-hex>`	`identity/agent_id.py`
Key algorithm	Ed25519	`identity/agent_id.py`
Credential TTL	15 min (configurable)	`identity/credentials.py`
SVID TTL	1 h (rotate at <10 min)	`identity/spiffe.py`
Handshake expiry	30 s nonce	`trust/handshake.py`
Handshake cache TTL	15 min	`trust/handshake.py`
Revocation propagation	≤ 5 s	`identity/credentials.py`
Sponsor max agents	10 (default)	`identity/sponsor.py`
Max delegation depth	3 (default)	`identity/sponsor.py`

4. Trust Scoring Model

5 Dimensions

pie title Trust Score Weight Distribution
    "Policy Compliance" : 25
    "Security Posture" : 25
    "Output Quality" : 20
    "Resource Efficiency" : 15
    "Collaboration Health" : 15

Dimension	Weight	Description
Policy Compliance	25 %	Adherence to governance rules
Security Posture	25 %	Credential hygiene, vulnerability posture
Output Quality	20 %	Task success rate, accuracy
Resource Efficiency	15 %	Compute/token usage vs. budget
Collaboration Health	15 %	Responsiveness, protocol compliance

Thresholds & Tiers

graph LR
    subgraph "Score Range 0–1000"
        U["Untrusted<br/>&lt; 300"]
        P["Probationary<br/>300–499"]
        S["Standard<br/>500–699"]
        T["Trusted<br/>700–899"]
        V["Verified Partner<br/>≥ 900"]
    end

    U -->|"revocation<br/>threshold"| P
    P -->|"warn<br/>threshold"| S
    S --> T
    T --> V

    style U fill:#ef4444,color:#fff
    style P fill:#f97316,color:#fff
    style S fill:#eab308,color:#000
    style T fill:#22c55e,color:#fff
    style V fill:#3b82f6,color:#fff

Threshold	Score	Action
Revocation	< 300	Credentials revoked, peer blacklisted
Warning	< 500	Alert raised, capabilities restricted
Allow	≥ 500	Normal operation
Trusted bridge	≥ 700	TrustBridge default threshold

Trust Decay Model

graph TD
    A[Agent Active?] -->|No positive signals| B[Decay: −2 pts/hr]
    B --> C{Score > 100?}
    C -->|Yes| D[Apply decay]
    C -->|No| E[Floor at 100]
    D --> F{KL divergence > 0.5?}
    F -->|Yes| G[Regime shift detected]
    F -->|No| H[Continue monitoring]
    G --> I[Propagate to neighbors<br/>factor: 0.3, depth: 2 hops]

Parameter	Value
Decay rate	2.0 pts / hour
Minimum floor	100
Propagation factor	0.3
Propagation depth	2 hops
Regime threshold (KL divergence)	0.5
Recent window	1 hour
Baseline window	30 days

Anomaly Detection

graph TB
    Behavioral[Behavioral Monitor] --> RF[Rapid-fire<br/>&gt; 10 actions / 5 s<br/>HIGH]
    Behavioral --> AD[Action Drift<br/>New actions post-baseline<br/>MEDIUM]
    Behavioral --> FS[Frequency Spike<br/>&gt; 3σ above mean<br/>MEDIUM]
    Behavioral --> TD[Trust Degradation<br/>≥ 15 % drop<br/>HIGH]
    Behavioral --> TOD[Time-of-Day<br/>&lt; 1 % baseline activity<br/>LOW]

    RF --> Feedback[Feed back into<br/>Trust Score]
    AD --> Feedback
    FS --> Feedback
    TD --> Feedback
    TOD --> Feedback

5. Governance Engine Flow

flowchart LR
    Req[Agent Request] --> PE[Policy Engine<br/>governance/policy.py]
    PE --> YAML{YAML/JSON<br/>Rules}
    YAML -->|< 5 ms| Verdict1[allow / deny / warn<br/>require_approval / log]
    PE --> OPA[OPA/Rego<br/>governance/opa.py]
    OPA --> Verdict2[Rego Result]
    Verdict1 --> Merge[Merge Verdicts]
    Verdict2 --> Merge
    Merge --> Comp[Compliance Mapping<br/>governance/compliance.py]
    Comp --> Audit[Hash Chain Audit Chain<br/>governance/audit.py]
    Audit --> CE[CloudEvents v1.0<br/>Azure Event Grid<br/>AWS EventBridge<br/>Splunk]

    Shadow[Shadow Mode<br/>governance/shadow.py] -.->|dry-run<br/>&lt; 2 % divergence| PE

Governance Submodules

Submodule	File	Purpose
Policy	`governance/policy.py`	Declarative YAML/JSON rules with rate limiting
OPA	`governance/opa.py`	Rego policy evaluation (Kubernetes-familiar)
Compliance	`governance/compliance.py`	Maps actions → EU AI Act, SOC 2, HIPAA, GDPR controls
Audit	`governance/audit.py`	hash chain tree audit chain, tamper-evident, CloudEvents export
Persistent Audit	`governance/persistent_audit.py`	Durable audit log storage
Shadow	`governance/shadow.py`	Test new policies in parallel; batch replay support

Policy Evaluation Order

sequenceDiagram
    participant Agent
    participant Policy as policy.py
    participant OPA as opa.py
    participant Audit as audit.py

    Agent->>Policy: evaluate(request)
    Policy->>Policy: match YAML/JSON rules<br/>(rate limit check)
    Policy->>OPA: forward to Rego<br/>(if configured)
    OPA-->>Policy: rego verdict
    Policy->>Policy: merge verdicts<br/>(most restrictive wins)
    Policy-->>Agent: allow | deny | warn
    Policy->>Audit: append to hash chain

6. Protocol Bridge Architecture

graph TB
    subgraph External Protocols
        A2A[A2A Protocol<br/>integrations/a2a/]
        MCP[MCP Protocol<br/>integrations/mcp/]
        IATP[IATP Protocol]
        ACP[ACP Protocol]
    end

    subgraph TrustBridge["TrustBridge (trust/bridge.py)"]
        VP[verify_peer]
        IPT[is_peer_trusted]
        RPT[revoke_peer_trust]
        GTP[get_trusted_peers]
    end

    subgraph "Framework Adapters"
        LF[LangFlow<br/>integrations/langflow/]
        LG[LangGraph<br/>integrations/langgraph/]
        SW[Swarm<br/>integrations/swarm/]
        FL[Flowise<br/>integrations/flowise/]
        HA[Haystack<br/>integrations/haystack/]
    end

    A2A --> VP
    MCP --> VP
    IATP --> VP
    ACP --> VP

    VP --> IPT
    IPT -->|"score ≥ 700"| GTP
    IPT -->|"score < 300"| RPT

    GTP --> LF
    GTP --> LG
    GTP --> SW
    GTP --> FL
    GTP --> HA

Bridge Operations

Operation	Method	Description
Verify peer	`verify_peer()`	Core trust gate — validates identity + trust score before communication
Check trust	`is_peer_trusted()`	Quick boolean check against threshold (default 700)
Revoke trust	`revoke_peer_trust()`	Instant trust revocation, propagated across mesh
List trusted	`get_trusted_peers()`	Filter peers by minimum trust score

Integration Submodules

Submodule	Path	Protocol
A2A	`integrations/a2a/`	Google A2A agent-to-agent
MCP	`integrations/mcp/`	Anthropic Model Context Protocol
AI Card	`integrations/ai_card/`	Signed discovery cards
LangFlow	`integrations/langflow/`	LangFlow orchestration
LangGraph	`integrations/langgraph/`	LangGraph state machines
Swarm	`integrations/swarm/`	OpenAI Swarm integration
Flowise	`integrations/flowise/`	Flowise flow builder
Haystack	`integrations/haystack/`	deepset Haystack pipelines

7. Storage Layer

graph TB
    subgraph Consumers
        ID[Identity Layer]
        TR[Trust Layer]
        GOV[Governance Layer]
        RW[Reward Layer]
    end

    subgraph "StorageProvider (storage/provider.py)"
        KV[Key-Value<br/>get / set / delete + TTL]
        Hash[Hash Ops<br/>structured data]
        List[List Ops<br/>audit logs, events]
        SSet[Sorted Sets<br/>trust score rankings]
        Atomic[Atomic<br/>incr / decr]
        Batch[Batch &amp; Pattern<br/>operations]
    end

    subgraph Backends
        Redis[(Redis<br/>storage/redis_provider.py)]
        PG[(PostgreSQL<br/>storage/postgres_provider.py)]
        Mem[(Memory<br/>storage/memory_provider.py)]
    end

    ID --> KV
    TR --> SSet
    GOV --> List
    RW --> Hash

    KV --> Redis
    KV --> PG
    KV --> Mem
    Hash --> Redis
    Hash --> PG
    Hash --> Mem
    List --> Redis
    List --> PG
    List --> Mem
    SSet --> Redis
    SSet --> PG
    SSet --> Mem

Parameter	Default
Connection timeout	30 s
Pool size	10

8. Cross-Repo Ecosystem

graph TB
    subgraph "agent-mesh"
        AM_ID[Identity Layer]
        AM_TR[Trust Layer]
        AM_GOV[Governance Layer]
        AM_RW[Reward Layer]
        AM_INT[Protocol Bridges]
    end

    subgraph "agent-os"
        AO_GK[Governance Kernel]
        AO_PE[Policy Engine]
        AO_RT[Runtime]
    end

    subgraph "agent-sre"
        AS_SLO[SLO Monitoring]
        AS_INC[Incident Management]
        AS_OBS[Observability]
    end

    AM_GOV -->|"policy sync"| AO_PE
    AM_GOV -->|"governance rules"| AO_GK
    AM_TR -->|"trust signals"| AO_RT

    AS_SLO -->|"SLO violations"| AM_TR
    AS_INC -->|"incident context"| AM_GOV
    AS_OBS -->|"metrics + traces"| AM_RW

    AM_INT -->|"agent discovery"| AO_RT
    AO_GK -->|"kernel policies"| AM_GOV

Integration Points

From	To	Data Flow
agent-mesh → agent-os	Governance → Policy Engine	Policy sync, governance rules
agent-mesh → agent-os	Trust → Runtime	Trust signals for agent scheduling
agent-mesh → agent-os	Integrations → Runtime	Agent discovery via AI Cards
agent-os → agent-mesh	Governance Kernel → Governance	Kernel-level policy constraints
agent-sre → agent-mesh	SLO Monitoring → Trust	SLO violations affect trust scores
agent-sre → agent-mesh	Incident Mgmt → Governance	Incident context for audit trails
agent-sre → agent-mesh	Observability → Reward	Metrics feed reputation engine

Related: agent-os · agent-sre · GitHub Issue: agent-os#263

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AgentMesh Architecture

1. Overview

Module Map

2. 4-Layer Trust Stack

Layer Details

3. Identity Lifecycle

Key Parameters

4. Trust Scoring Model

5 Dimensions

Thresholds & Tiers

Trust Decay Model

Anomaly Detection

5. Governance Engine Flow

Governance Submodules

Policy Evaluation Order

6. Protocol Bridge Architecture

Bridge Operations

Integration Submodules

7. Storage Layer

8. Cross-Repo Ecosystem

Integration Points

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ARCHITECTURE.md

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AgentMesh Architecture

1. Overview

Module Map

2. 4-Layer Trust Stack

Layer Details

3. Identity Lifecycle

Key Parameters

4. Trust Scoring Model

5 Dimensions

Thresholds & Tiers

Trust Decay Model

Anomaly Detection

5. Governance Engine Flow

Governance Submodules

Policy Evaluation Order

6. Protocol Bridge Architecture

Bridge Operations

Integration Submodules

7. Storage Layer

8. Cross-Repo Ecosystem

Integration Points