Reload launches Epic to manage AI "teammates," secures $2.275M to scale
There came a moment when Newton Asare realized AI agents weren't just tools. "They acted as real team members." He and Kiran Das started treating them like hires-assigning work, checking outputs, and expecting progress over time.
If that's the future, product teams need governance: onboarding, roles and permissions, performance tracking, and shared context that survives across sprints, agents, and handoffs. That's the premise behind Reload, which just raised $2.275 million led by Anthemis, with participation from Zeal Capital Partners and Plug and Play.
What Reload is
Reload is a platform to manage AI agents across teams and divisions. Connect agents from any source-third-party or internal-assign roles and permissions, and track the work they perform. Think of it as a registry and control layer for AI workers: visibility, coordination, and oversight in one place.
Why Epic matters to product development
Most coding agents can write and refactor code, but they operate with short-term memory. Over time they lose product context and drift from the original intent. That's expensive for teams and risky for systems.
Epic, built on top of Reload, acts like the system architect working alongside coding agents. It defines product requirements and constraints upfront, then continually reminds agents what they're building and why-preserving coherence as the codebase evolves. It doesn't replace coding agents; it makes them more effective.
- Creates core system artifacts at project kickoff: product requirements, data models, API specs, technology stack choices, diagrams, and structured task breakdowns.
- Maintains structured memory of decisions, code changes, and templates that follows you even if you switch agents.
- Enables multiple engineers using different agents to work from a single source of truth.
Epic integrates into developer workflows as an extension for AI-enabled editors like Cursor and Windsurf, collaborating with the agents inside those tools.
Governance for AI workers
Reload treats agents like real contributors: they're onboarded, given scoped permissions, and monitored. You get the auditability and coordination product orgs expect-across code, context, and decisions-not just single-session prompts.
Competitors exist-LongChain and CrewAI among them-but Reload differentiates by defining the system upfront and maintaining project-level context between agents and sessions. Asare put it simply: "Traditional HR systems were not designed for AI agents who work as team members." That's the level they're focused on.
Event and what's next
Event - Boston, Massachusetts | June 23, 2026.
The new funding goes to hiring and product development, including expanding infrastructure to support a growing number of AI agents. "We are building for the next era of work."
How product leaders can put this to work
- Identify workflows where agents already perform human tasks (e.g., code generation, refactoring, QA). Document owners and guardrails.
- Define a governance model: roles, permissions, review steps, and escalation paths for AI workers.
- Pilot Epic on a new module or greenfield project to seed the shared system context from day one.
- Set success metrics upfront: cycle time, defects escaped, rework due to context loss, and handoff friction across agents.
- Integrate with your team's editors and CI so context and decisions are captured where work happens.
Want a deeper foundation for your team? Explore AI for Product Development or map skills with the AI Learning Path for Software Engineers.
AI workers are here. The teams that win won't be the ones with more agents-they'll be the ones who manage them like a product.
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