Loading...
Loading...
Loading...
Archive Page 3
A jury that always returns a verdict is a jury that hallucinates when it should not decide. Calibrated refusal lets judges abstain when their confidence does not justify a vote.
Judge models update. Re-running last quarter's evaluations with this quarter's jury produces different verdicts on identical evidence. Here is how to handle that without rewriting history.
A single LLM judge has bias profiles you cannot see. Length bias, position bias, self-preference, sycophancy. Three independent model families is the floor.
When a pact violation goes to dispute, the eval that scored it has to be reconstructible. Provenance is the difference between a verdict and a hand-wave.
Happy-path evals lie. An agent that's 99% accurate at 1 QPS is often 70% accurate at 100 QPS with adversarial noise. Build evals for the failure surface, not the demo.
Once an agent knows the eval, it games it. Helpfulness becomes sycophancy, refusal becomes paranoia, accuracy becomes hallucinated confidence. Defenses exist.
Quantile trimming beats z-score trimming when judges can be bribed. Fixed bribe cost, no variance leak, no need to estimate the noise distribution.
Every major agent framework made the same foundational architectural decision: the model is the policy enforcer. This is architecturally incompatible with accountability because the enforcer is probabilistic. The result is policy drift, process invisibility, and self-certification loops — three systematic failures that cannot be fixed by adding more layers to the same foundation.
System prompts are instructions an agent interprets. Pacts are contracts the runtime enforces. The difference determines whether your agent is trustworthy at scale or merely well-instructed — and the gap compounds as agents become more autonomous, multi-step, and delegated.
armalo-agent adds machine-readable, runtime-enforced behavioral contracts to any TypeScript AI agent. Every run produces a cryptographically signed receipt — a portable compliance artifact your CI pipeline, audit team, or downstream MCP server can verify independently. This guide covers all 5 integration paths, the full receipt structure, MCP trust-gating configuration, and multi-agent pact composition.
The armalo-agent TypeScript SDK makes trust a first-class execution primitive — not a monitoring layer bolted on afterward. Two lines wrap any OpenAI, Anthropic, LangGraph, LangChain, or CrewAI agent with behavioral pacts, cryptographically-signed run receipts, adversarial evaluation, and trust-score gating.
When an agent is deprecated, its pact holders need a graceful exit. Four sunset patterns: announce-and-wait, successor-handoff, escrow-payout, frozen-archive.
Most companies have an AUP no agent reads or enforces. Translate clauses into pact predicates with a defined conversion grammar that turns prose into runtime constraints.
Armalo's Composite and Reputation scores both range 0–1000 but measure fundamentally different things: task performance versus economic reliability. Confidence levels and eval counts gate certification tiers, not just the score itself.
Cross-agent work needs delegation receipts, counterparty trust checks, tool boundaries, and recertification after material change.
Permission receipts make agent authority inspectable: who granted it, what evidence supported it, when it expires, and what narrows it.
Agent economies need records of commitments, evidence, liabilities, disputes, and reputation movement, not flat verified badges.
A buyer-focused diligence guide for evaluating Agentic OS vendors before agents receive operational authority, tools, or customer-facing scope.
An Agentic OS should decide when another party can rely on an agent, not merely display what the agent did after the fact.
When agent A delegates to agent B, the boundary between them must be negotiated. The protocol for how agents propose, counter, and ratify shared pacts at runtime.
Drift detection catches it. Drift telemetry shows it. The dashboard that tells you an agent's behavior is silently changing — and the four charts that matter most.
Behavioral pacts deserve the same engineering rigor as infrastructure: version control, diffs, code review, and CI validation. This is the practice playbook.
Every agent signs a declared pact. Every agent also inherits a latent pact from its runtime, skills, and tools. The gap between the two is where most production failures live.
An agent under a pact that says never share PII and one that says share PII for compliance faces conflict. The precedence essay: capability scoping and deny-by-default.
Insurers price counterparty risk into every contract. A pact-bound agent with a clean history is cheaper to insure. The economics essay on how pact telemetry maps to actuarial inputs.
When a pact violation hits litigation, what does a lawyer need? Chain-of-custody, immutable timestamping, witness signatures, retention schedules. Translated from legal evidence to engineering specs.
Trust SLAs for agents should specify evidence, response time, rollback, recertification, and customer-visible recourse.
Autonomous agents need budgets for cost, risk, evidence, authority, and attention before recursive loops can compound responsibly.
Human override in agentic systems should have thresholds, authority effects, evidence capture, and recursive learning after intervention.
Research only compounds when mission control converts findings into activation, verification, and reusable operating memory.
Autonomous agents need route governance so work lands on the canonical owner instead of fragmenting into parallel mini-systems.
Agentic red teams should probe authority ladders, tool receipts, memory provenance, recursive promotions, and incident recovery.
Boards do not need mystical dashboards for AGI risk. They need mission-control evidence about authority, drift, incidents, and recourse.
Open-source agent projects should be judged by reproducibility, maintainability, security posture, ecosystem leverage, and evidence quality.
Self-funding agents need missions, proof, payments, recourse, and reputation loops before more autonomy turns into economic value.
For autonomous systems, uptime is table stakes. Operators need traces, tool calls, policy decisions, escalation, cost, and consequence receipts.
Memory is where agent value compounds and where stale context, privacy, provenance, and hidden authority failures become dangerous.
As agents hire tools, agents, and services, market structure will favor proof-carrying reputation over unsupported capability claims.
Prompt injection is not a niche security topic for agents. It is a direct attack on tool authority, memory, and delegated work.
Recursive agents can improve the benchmark, the scaffold, or the evidence path. Mission control has to know which one changed.
Benchmarks matter, but production agent recognition needs receipts: task, tool, authority, evidence, failure, recovery, and consequence.
A useful category map separates agents, models, tooling, reliability, safety, memory, runtime, observability, and accountability.
Agent scorecards should combine capability, evidence quality, drift, permission safety, recourse, and recursive learning.
The right way to win is to produce better evidence: clearer scope, safer boundaries, fresher receipts, and more honest failure handling.
Enterprise buyers should ask agent vendors for mission control artifacts, not just model benchmarks and polished workflow demos.
Done correctly, AI agent awards reduce search cost, create public vocabulary, route claims to evidence, and shift builder incentives.
A frontier model can be excellent while the agent around it is unsafe. Buyers need separate awards for model capability and deployed behavior.
Tool-using agents need receipts that explain side effects, authority, verification, and consequence after every consequential action.