The Context Engineering Gap Nobody's Talking About

Why I Spent Six Weeks on This

I just spent six weeks evaluating AI agent platforms. Six weeks of documentation rabbit holes, half-broken demos, proof-of-concept builds that worked beautifully at message three and fell apart at message thirty, and a slowly accumulating conviction that most comparison articles are written by people who've never actually shipped an agent to production.

If you're reading this, you're probably about to start the same journey. Maybe your company just greenlit an AI initiative. Maybe your CTO saw a LangGraph demo and got excited. Maybe—like me—you're the one who'll actually have to make this work in production, with real users, real data, and real regulatory requirements.

Every platform comparison I found focuses on the same things: how many LLM providers they support, how pretty the visual builder is, how many GitHub stars they have. None of them asked the question that actually matters: what happens when your agent runs out of context?

That question consumed my evaluation. And the answers varied from "we don't handle that" to "here's our six-layer middleware stack for it." The gap between those responses is, I think, the most important thing separating serious agent platforms from expensive toys.

The Market in February 2026

Before I get into the platforms, let me ground this in reality. The AI agent space right now is simultaneously overhyped and underbuilt.

Gartner projects 40% of enterprise apps will have embedded AI agents by end of 2026—up from under 5% in early 2025. The market is projected to hit $52.62 billion by 2030. But here's the number that should sober you up: according to Capgemini, only 2% of organizations have deployed agents at scale.

Meanwhile, the LangChain 2025 survey of 1,300+ professionals found that while 57% have agents in some form of production, the number one barrier isn't cost, isn't security, isn't even model capability. It's quality. 32% cited output quality as their top challenge. And when you dig into what "quality" means, it almost always comes back to the same thing: the agent losing track of what it's supposed to be doing because its context got corrupted, bloated, or simply ran out of room.

The market is also splitting in two: visual/no-code builders aimed at business users, and full-stack developer frameworks for engineering teams. Increasingly, the platforms that matter are the ones trying to bridge both sides.

The Context Problem Nobody Demos

Here's what I mean by context engineering, and why it changed how I evaluate platforms.

Every LLM has a context window—128K to 200K tokens for current models. Into that window, you need to fit: the system prompt, tool definitions, conversation history, tool call results, retrieved documents, and the actual user query. In a demo, this is trivial. In production, it's the whole game.

Consider a real scenario: a customer support agent for a bank. The agent has 20 tools available (account lookup, transaction search, loan calculator, etc.). Each tool definition consumes ~500 tokens. That's 10,000 tokens just for tool definitions before a single message is exchanged. Add the system prompt (2,000 tokens), conversation history that grows with each turn, and tool results that can be massive (a transaction search returning 50 rows is easily 8,000 tokens).

By turn 15 of a conversation, you're burning through 80K+ tokens. By turn 30, you're hitting the ceiling. And when you hit the ceiling, your agent doesn't gracefully degrade—it starts hallucinating, forgetting earlier context, or simply breaking.

Context engineering is the set of techniques that prevent this collapse. It includes conversation summarization, tool result pruning, intelligent tool selection (only load the 5 most relevant tools per turn), memory offloading to vector stores, and PII redaction before storage. The platforms that have invested in this are the ones building for production. The ones that haven't are building for demos.

The Platforms, Ranked by Context Maturity

After testing all ten platforms, I grouped them into four tiers based on how seriously they treat context engineering. This isn't the only axis that matters, but it's the one that predicted real-world viability better than any other.

The Comparison Table

I've limited this to the five platforms I'd actually consider for a serious project. The columns that matter most, in my opinion, are highlighted.

FLOWX Agent Builder: The Deepest Context Stack

I want to be clear: I'm not saying FLOWX is the best platform for every use case. I'm saying it has the most complete answer to the context engineering problem, and for my use case—building agents for a regulated financial services company—that matters more than anything else on the feature list.

Visual Workflows That Actually Compile

FLOWX is the only platform where the visual node-and-edge designer compiles to production-grade LangGraph StateGraphs. This isn't a visualization layer on top of code (like LangGraph Studio) or a JSON config exporter (like Dify or Flowise). The workflow you design visually is the production artifact. It goes through topological sort, dependency resolution, and execution phase planning. The 39 node types across 8 categories cover agents, text processing, document handling, data operations, RAG, and flow control.

Two workflow creation paths exist: manual drag-and-drop (for engineers who want control) and an AI-generated mode where a deep agent designs the workflow from a natural language description. The AI path includes human-in-the-loop checkpoints—the agent asks you clarifying questions, shows you a preview, and only saves after your approval. Then it auto-generates synthetic test data and runs a live test. I've never seen another platform do that during workflow creation itself.

The Six-Layer Context Stack

This is the core differentiator. FLOWX has six context engineering middlewares that run as an automated pipeline:

1. Summarization Middleware — When conversation tokens exceed 4,000, older messages are LLM-compressed into a summary while preserving the 20 most recent messages. It's smart about not splitting AI/tool-result pairs.
2. Context Editing Middleware — At 100,000 tokens, old tool results are cleared (replaced with [cleared] placeholders) while keeping the 3 most recent results intact. This alone recovers tens of thousands of tokens.
3. Tool Selection Middleware — Instead of loading all 20 tools (10,000 tokens), an LLM selects the 5 most relevant tools per turn. Saves 7,500 tokens per invocation.
4. Skill Injection Middleware — Domain knowledge (packaged as Markdown + YAML frontmatter) is loaded dynamically based on the current conversation context. No other platform has anything like this.
5. Human Handoff Middleware — When escalating to a human agent, context is intelligently trimmed to 4,000 tokens with a generated summary and extracted key points.
6. Three-Tier Memory — Episodic memory (specific events), Semantic memory (knowledge triples: subject-predicate-object), and Procedural memory (agent behaviors and rules). Google Vertex has episodic memory; nobody else has all three.

Each middleware is a composable addon. You enable them per node. The system runs them in sequence on every model invocation. This isn't a feature you configure once; it's a runtime pipeline that keeps your agent healthy across hundreds of turns.

76 Domain-Specific Tools

FLOWX ships 6 MCP toolkits targeting regulated industries: Banking (15 tools), Insurance (13), Tax (12), Logistics (12), Document Processing (14), and OCR Confidence Scoring (10). Nobody else ships domain toolkits. For my use case in financial services, having loan amortization, EMI calculation, mortgage comparison, and credit card payoff tools out of the box saved weeks of development.

AI-Generated Custom Tools

Describe a tool in natural language, and FLOWX generates validated Python code with AST-based security analysis, import whitelist enforcement, sandboxed execution, automatic JSON Schema extraction from type hints, and hot reload without restart. This is genuinely novel. Every other platform requires you to hand-code tools.

Deep Agents

Inspired by Claude Code and Manus, FLOWX's Deep Agents handle multi-step autonomous tasks with planning (TODO decomposition), file system operations, subagent delegation, and persistent memory across sessions. They use 4 storage backends including a Composite backend that routes different paths to different persistence layers—ephemeral scratch space alongside persistent cross-session memory. The harness automatically evicts large tool results to file storage, summarizes history at 85% context capacity, and repairs interrupted tool call sequences.

Where FLOWX Falls Short

I promised honesty. Here's what held FLOWX back in my evaluation.

• No browser/computer automation. Google has Computer Use, AWS has Nova Act, UiPath has decades of RPA. FLOWX has nothing. If your agents need to interact with web UIs, this is a dealbreaker.
• Only 7 LLM providers. Anthropic, OpenAI, Google, Azure, Mistral, xAI, and Ollama. That covers 95% of use cases, but CrewAI supports 100+ via LiteLLM, and Google Vertex offers 200+ models. If you need exotic providers, FLOWX won't have them.
• Proprietary, not open source. LangGraph is MIT. CrewAI, Dify, and Flowise are all open source. FLOWX is not. For organizations with strict OSS mandates, this is a non-starter. The estimated replication cost ($1.5M-$3M over 12-24 months) suggests the IP has real value, but you're committing to a vendor.
• PII detection covers ~14 entity types. AWS Bedrock supports 30+ with ML-based detection. If you need comprehensive PII handling across many entity types, Bedrock has a deeper library.
• Partial time-travel debugging. LangGraph's full replay/rewind is the gold standard here. FLOWX has PostgreSQL checkpointing for resumable executions, but not the same level of execution replay.

These are real trade-offs, not spin. The browser automation gap is the most significant because it limits the types of agents you can build. The provider count is a minor issue in practice—the seven supported providers include every model most teams actually use. The open source question depends entirely on your organization's procurement policies.

How to Actually Choose

After six weeks, here's my decision framework. It's not complex because the right answer depends on surprisingly few questions.

• You need to connect AI to existing API workflows

  Use n8n. It's not an agent platform, but with 1,000+ integrations, nothing beats it for API orchestration with AI nodes bolted on. Community edition is free. Don't overthink it.

• You want maximum flexibility and have strong Python engineers

  Use LangGraph. It's the most powerful framework, the time-travel debugging is unmatched, and the MIT license means you own everything. Budget for the learning curve and for building your own context management.

• You're building for regulated industries and need production context management

  Use FLOWX Agent Builder. The domain toolkits, context engineering stack, PII detection, and visual-to-LangGraph compilation were built for this exact scenario. Accept the vendor commitment.

• You're already deep in a cloud ecosystem

  Use Google Vertex AI (GCP) or AWS Bedrock (AWS). Both are building toward complete agent platforms. Google's ADK is more developer-friendly; AWS has more enterprise features. Accept the lock-in if you're already locked in.

• You need a quick, self-hosted RAG chatbot

  Use Dify. 100K GitHub stars, excellent visual builder, strong RAG, and Apache 2.0 licensed. It won't scale to complex multi-agent systems, but for RAG-powered chatbots and simple agents, it's the fastest path to production.

My Verdict

The AI agent market is going through its "JavaScript framework" era. Too many options, too much marketing, and not enough focus on the hard engineering problems. Context engineering is the hardest of those problems, and it's the one that determines whether your agent works at turn 5 or at turn 500.

Most platforms don't even acknowledge this problem exists. They show you a three-turn demo, point to the LLM's context window size, and call it solved. It's not solved. Managing context across long conversations, across tool calls that return massive payloads, across sessions that span days—that's where production agents live or die.

FLOWX is the only platform I found that treats context engineering as a first-class concern with automated middleware, not just documentation telling you to build it yourself. That doesn't make it perfect—the missing browser automation and closed source are real limitations—but for teams building agents that need to actually work in production, especially in regulated environments, the context engineering stack alone justifies the evaluation.

The gap will close. LangGraph will probably build visual tooling eventually. Google will probably expand their memory system. CrewAI will probably add context middleware. But right now, in February 2026, the gap exists. And if you're shipping agents to production this quarter, "probably eventually" doesn't ship.

Build your agents for turn 500, not turn 5. Everything else follows from that.