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Photo: Rayi Christian Wicaksono (CC0)
What you are probably seeing
Every time a task moves from intake to review, you paste the same client details and history into a new prompt or form. Your team spends hours duplicating work instead of making progress. The friction shows up as higher error rates because key details get dropped during handoffs.
You notice that your pipeline stalls at transition points. Users abandon workflows not because they lack intent, but because rebuilding the necessary background feels tedious. Your metrics likely show a drop-off specifically at step boundaries where context resets.
Why it happens
Most current systems treat each step as an isolated event. They lack a shared memory layer that persists state across different functions. This architectural gap means the system cannot see what happened previously, so it demands fresh input to ensure accuracy. It is a design choice that prioritizes safety over fluidity.
The lack of a unified context store creates a manual bottleneck. When agents or tools do not share a common source of truth, human operators become the bridge. This forces users to act as data pipes, copying and pasting information between disconnected tools, which defeats the purpose of automation.
What changed recently
2026-10-07 · Compare Top 10 Service Orchestration Platforms
Capterra lists new AI tools for that gap:
• Stonebranch sends OpenTelemetry workflow data to APM tools
• BMC Control-M AI workflow creator drafts structures from natural language
• JAMS Scheduler AI agent troubleshoots failures and finds jobs
Source: AIMultiple
2026-10-07 · Why Cisco Continues to invest in AI Native RunOn: Bringing Context, Visibility, and AI to Multi-Cloud
Cisco is now unifying multi-cloud views for that gap:
• One view of public and private cloud data
• Auto-detects roles to show relevant context
• Provision and monitor in one control plane
Source: Cisco Blogs
2026-10-07 · Precisely Brings Trusted Data Management and Customer Communications to AI Agents With No Custom Integration Required
Precisely is now connecting trusted data to AI agents for that gap:
• MCP servers connect AI agents to data tools without custom integration.
• Precisely EngageOne handles customer communication workflows.
Source: PR Newswire
2026-10-06 · Mistral Large 4
Mistral is now addressing that gap with:
• 1.05T total parameters, 49B active
• Built-in Agents and Conversations for workflows
Source: hnrss_front
2026-10-06 · Model Context Protocol: What it is and why it matters
Anthropic is now fixing that gap with MCP:
• open standard for AI tool connections
• supports real-time data access
• reduces custom integration work
Source: SAS: Data and AI Solutions
2026-10-06 · The missing layer in your AI data architecture: Giving AI agents the context to act
IBM and SAS are now using Model Context Protocol for that gap:
• 88% of IT leaders say streaming platforms unblock agentic AI progress
• 72% of executives cite insufficient infrastructure for real-time data processing
Source: IBM
Step by step
1. Define a single source of truth for all workflow data that every step can read and write to.
2. Map out where context is currently lost between steps and identify the specific fields that need to persist.
3. Implement a handoff protocol that passes structured data objects instead of raw text snippets between agents.
4. Test your workflows by removing all manual input fields at transition points to see where the process breaks.
5. Train your team to rely on the automated context flow rather than forcing re-entry when the system state is correct.
Ways to go further
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