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