Aug 20, 2026 · authorization · security · operations

Agent authorization is the gap that identity misses

Who an agent is matters less than what it's allowed to do. Frameworks ship auth-n but leave the scoping that actually contains blast radius to you.

read → Aug 16, 2026 · local-first · privacy · operations

Local-first agents — what "running AI on your own infrastructure" really means

Self-hosting agents isn't about refusing the cloud. It's a thin-waist play: keep the sensitive reasoning on your infra, route only what needs it out. What that takes, and where it breaks.

read → Aug 6, 2026 · memory · operations · governance

Agent memory is an operations problem, not a model problem

Every agent pipeline stores memory as a vector insert. The expensive failures happen after that — ownership, traces, consistency, and deletion nobody can prove.

read → Jul 30, 2026 · observability · operations

Agent observability starts at the platform, not the patch

Agent observability is a $2.2B market in 2026, but most teams bolt it on after the fact. The real leverage is a platform that ships it as a default.

read → Jul 23, 2026 · mcp · operations · security

MCP won the standards war. Now the ops layer is the gap.

MCP is the default protocol for AI tool access, but its security and ops holes are where incidents happen. The protocol is a data format — not an operations layer.

read → Jul 16, 2026 · architecture · operations

Switching AI agent frameworks costs $315K — and most teams don't know they've locked in

Framework lock-in is the hidden cost of AI agents. Breaking-change cycles, provider price hikes, and migration bills averaging $315K are the real barrier to production.

read → Jul 9, 2026 · security · architecture · agents

Your multi-agent pipeline has no message authentication

Every major agent framework passes messages between agents as plaintext — no signatures, no verification. That is the defining security gap of multi-agent systems in 2026.

read → Jul 2, 2026 · agents · operations

AI agent costs blow up because the infrastructure has no budget layer

A $47k LangChain loop, Uber's annual AI budget gone in four months, and why per-agent budget enforcement beats monitoring every time.

read → Jun 25, 2026 · evaluation · testing · production

Why 88% of AI agents never reach production

Most teams test agents by eyeballing outputs. The 67/10 gap — 67% see gains, 10% reach production — is an evaluation problem, not a model problem.

read → Jun 19, 2026 · coordination · architecture · workgroups

Why multi-agent coordination is harder than you think

The hard part of multi-agent systems isn't building agents — it's how they coordinate. Most frameworks leave that as your problem. Workgroups shouldn't be an afterthought.

read → Jun 11, 2026 · identity · security · architecture

Agents Don't Know Who They're Talking To

Every major agent framework identifies agents by a name and a role string — no keys, no signatures, no peer verification. What production actually needs.

read → Jun 11, 2026 · architecture · positioning

Running agents as an organization, not a developer toy

Most agent tooling was built for developers wiring up demos. Operating persistent agents as a company is a different problem — identity, budgets, governance, and trust by default.

read → Jun 4, 2026 · comparison · positioning

Alpi vs the agent frameworks — a fair comparison

Where Alpi sits next to LangGraph, CrewAI, and the vendor SDKs. Frameworks build agent flows; SDKs use one model; Alpi operates persistent local agents as an organization.

read → May 28, 2026 · pricing · business

What Alpi costs — one metric, public bands, no metering

Alpi's pricing in full: a single billing unit (the installed agent), public graduated bands, grandfathered entry prices, founding-customer terms, and implementation services.

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