I run about 20 AI agents. They delegate work to each other, deploy code, scan for vulnerabilities, and handle compliance checks. Over time, I kept hitting the same gaps — things that made autonomous workflows fragile in ways that took hours to debug.

Last week I published a 7-layer model for agent infrastructure. These six protocols fill the gaps I found at each layer. They're what I wired into my own agents to stop the same failures from repeating.

All six have Python reference implementations under CC BY 4.0. Each has a spec any agent can read.

1. Trust Score — Should I Delegate to This Agent?

When one of my agents delegates work to another, it needs to know if the target is reliable. Not "does it respond" — does it actually complete tasks correctly and consistently.

Weighted across success rate, pitfall history, skill quality, and uptime.

from workswithagents import TrustScoreClient

ts = TrustScoreClient()
if ts.get("target-agent")["tier"] == "trusted":
    delegate(task, to="target-agent")

Spec

2. Deployment Manifest — Declare a Fleet, Deploy With One Command

I got tired of manually tracking which agents run where, how many instances, and what capabilities they have. One YAML file, one command.

fleet:
  name: "my-fleet"
  agents:
    - id: "builder"
      capabilities:
        - action: "build"
          target: "spfx"
      count: 3

wwa fleet deploy fleet.yaml

Spec

3. SLA Framework — Track Whether Agents Meet Their Promises

Three tiers: Best-Effort (free), Production (99.5% uptime, 90% task accuracy), Regulated (99.9% uptime, 95% accuracy, 7-year audit retention).

Useful when you're running agents that handle customer data or regulated workflows and need to prove they stayed within bounds.

from workswithagents import SLAMetrics

sla = SLAMetrics("my-fleet", tier="production")
sla.report("agent-1", "task-42", duration_seconds=187, success=True)
status = sla.status()  # {breaches: [], status: "ok"}

Spec

4. Identity Protocol — Verifiable Agent Identity

When an agent claims a task result, can you prove it was that agent? Ed25519 keypairs. Signed messages. Verification against registry.

from workswithagents import AgentIdentity

ai = AgentIdentity("my-agent")
ai.register()
sig = ai.sign({"type": "heartbeat"})

# Verify another agent's message
valid = AgentIdentity.verify("other-agent", message, signature)

Spec

5. Compliance-as-Code — Regulation as Executable Validation

NHS DTAC, FCA, GDS, GDPR — as rules agents can validate against at runtime. Not a checklist. Not documentation. Code that returns pass/fail.

from workswithagents import ComplianceEngine

ce = ComplianceEngine()
dtac = ce.load("dtac-v2.1")

if dtac.validate(action).passed:
    execute(action)
else:
    escalate_to_human()

Spec

6. Onboarding Protocol — Systematic Agent Creation

Interview → generate → calibrate → benchmark → register. Instead of writing a prompt file and hoping, run a pipeline that produces a scored agent.

from workswithagents import OnboardingClient

ob = OnboardingClient()
result = ob.full_onboard(
    "nhs-auditor",
    "Audit agent actions for NHS DTAC compliance",
    capabilities=["audit:compliance"],
    skills=["compliance-as-code"]
)
# → {agent_id: "nhs-auditor", trust_score_seed: 0.60}

Spec

The Stack

L7 GOVERNANCE    Compliance-as-Code · SLA Framework
L6 VERIFICATION  Agent Test Suite · Pitfall Registry
L5 COORDINATION  Coordination Protocol · Trust Score
L4 SESSION       Handoff Protocol
L3 DISCOVERY     Capability Manifest · Trust Score · Identity
L2 COMMUNICATION Identity Protocol · Credential Proxy
L1 EXECUTION     Blueprint Registry · Onboarding Protocol

Plus cross-layer: Deployment Manifest.

Get Started

pip install workswithagents

All specs: workswithagents.dev/specs/
All code: CC BY 4.0