Newsletter

Alio IT — Monthly Insights

July 2026

Engineering Under Pressure

This month our team was on the ground at the Arizona Aerospace Summit, talking with defense, aviation, and advanced engineering leaders about a challenge we hear constantly: modernizing legacy systems without breaking strict compliance (NIST / CMMC). That tension — move fast, stay compliant — is where a lot of our current client work lives.

Connecting with aerospace & tech leaders in Arizona.

We also submitted our application to the Claude Partner Network, aiming to bring deeper LLM integration and agentic AI frameworks into exactly these kinds of complex, compliance-heavy environments.

Book a 1:1 call →

Team & Culture: World Cup Bingo ⚽

High-performing engineering teams need strong connective tissue. Outside of sprint cycles and client deliverables, our remote team gathered for a World Cup-themed bingo competition — a fun excuse for some friendly competition, a recharge, and celebrating our wins together.

AI & Development: Anti-Patterns We're Watching in Agentic Builds

Building reliable AI agents means engineering around the model's actual signals — not guessing at its intent. In our team's ongoing work with agentic frameworks, three anti-patterns keep showing up when developers try to control an AI's task loop with shortcuts instead of the structured signals the API already provides:

• Natural language parsing — trying to infer what the model “meant” by scanning its text output, instead of checking the structured stop_reason the API returns.
• Iteration caps as primary stops — relying on a maximum loop counter to end a task, rather than treating it as a safety valve behind a proper completion signal.
• Content-type checking — breaking a loop as soon as any text appears, which ignores that a single response can mix text with other content blocks.

The common root cause: substituting a hacky heuristic for the structured signal the API already gives you. It's a good reminder for any team building agentic tooling — read the API's actual state, don't parse around it.

Tools We're Watching: Cutting Token Spend

• Honcho — a memory layer for AI agents. Instead of re-sending an entire conversation history as context on every call, Honcho stores messages and reasons about them in the background, then lets an agent query for just the relevant insight when it needs it. That's a meaningful lever on token spend, since you're pulling targeted context instead of paying to re-process full history each turn.
• pxpipe — an open-source local proxy that takes a different approach: it renders bulky text context (code, JSON, tool output) as compact images before sending it to the model, since dense text can pack more information per image-token than per text-token. It's an early-stage project worth keeping an eye on for teams looking to trim context costs on large, code-heavy sessions — we'd want to see it validated on our own workloads before relying on it in production.

Framework Watch: LangGraph vs. Strands Agents

Two different philosophies for building AI agents worth knowing the tradeoffs on:

• LangGraph takes a graph-driven approach — you explicitly define nodes, edges, and a typed state object, so every decision path is inspectable, auditable, and reproducible. That makes it the stronger pick for workflows needing branching, cycles, approval steps, or strict step ordering — common requirements in regulated environments.
• Strands Agents, AWS's open-source SDK, takes a model-driven approach instead: give it a model, a system prompt, and a list of tools, and the framework lets the LLM itself drive the reasoning loop with minimal scaffolding. It's built for speed of development and pairs natively with AWS Bedrock AgentCore for hosted runtime, identity, and observability — AWS has adopted it as a first-class option for AgentCore deployments.

Both are free, both support multiple model providers, and both can deploy on AgentCore — the choice isn't really about the runtime, it's about how much explicit control your workflow needs versus how much you're comfortable letting the model drive. We're seeing teams increasingly combine the two: Strands agents embedded inside LangGraph nodes, getting LangGraph's macro-level orchestration alongside Strands' lighter-weight reasoning at each step.

Recent work

• Platform Reliability at Time4Learning — scaling cloud infrastructure for high-demand edtech. Read the case study →
• AI-First QA Automation — modernizing test coverage to accelerate engineering velocity. Read the case study →

Client Spotlight: NinjaOne

We're proud to share a strong review from NinjaOne — now one of the biggest names in MSP/IT management software, serving tens of thousands of customers worldwide. Their Senior Automation Engineer highlighted how our team helped reduce their regression testing backlog and improve the stability and coverage of their automation suite, noting our engineers integrated quickly and delivered immediate value. Read the full review on Clutch →

Behind the scenes

• We're rolling out an upgraded go-to-market initiative to help more scale-ups solve engineering and QA bottlenecks.
• We're revamping our internal cybersecurity guidelines and compliance procedures to keep pace with evolving cloud threats — full breakdown next month.

Have a project or infrastructure bottleneck you'd like to talk through? Reply by email or book a call with our team.

Alio IT Solutions — Secure AI | Cloud Infrastructure | Custom Software Engineering