Your agents are slow and wrong,
for the same reason.
Without a shared context layer, every agent reconstructs your system on every call, crawling repos, grepping docs, querying ServiceNow, Archer, and your scanners, burning time and tokens, and still landing on a stale, partial picture.
DevGrid serves the live engineering graph and your rules instantly, pre-assembled, so agents skip the re-fetch and act on the truth the first time.
Faster, cheaper, and right.
AI moves fast. It also moves
on the wrong context.
Architecture knowledge is in one tool. Ownership in another. Dependencies, runtime, vulnerabilities, EOL, and the policies that govern them are scattered across several more.
Agents infer across the fragments and return confident answers that miss what matters: the service that's actually deployed, the owner who actually exists, the NFR they just violated. Teams are rolling out agents faster than their architecture knowledge can keep up.
Ungrounded agents aren't fast.
They pay a tax on every call.
Re-deriving context per invocation costs three ways. Latency (the agent crawls before it acts), tokens (you pay to re-read the same repos and docs again and again), and drift(each reconstruction is slightly different, so results aren't reproducible, a problem of its own in a regulated shop). Centralize context once and serve it to every agent, and the tax disappears. The agent starts from the answer instead of working toward it.
The difference isn't the model.
It's the context.
Ground → Scope → Act → Review.
The only context layer that makes your agents faster, and right.
Live context, pre-assembled and served instantly.
Services, dependencies, ownership, health, vulnerabilities, EOL, plus architecture decisions, standards, and NFRs. Fetched once, not re-crawled on every call.
Only the context the task needs.
Task-scoped delivery for incident triage, migration, or dependency upgrade, so agents act with precision, not a context dump.
Your tools write the fix. DevGrid makes them right.
Plug into Claude, Codex, Cursor, GitHub Copilot, and internal automations via MCP, API, or native integration. DevGrid doesn't replace them, it grounds them.
Approvals stay where they already live.
Checks, approvals, and rejection paths stay in your existing CI, policy engines, and human review. Closure flows back into the graph automatically.
The right rules, for the
task in front of them.
Anyone's agent can read a repo. What it can't read is your encoded rules: architecture decisions, standards, NFRs, security policies. DevGrid doesn't dump all of them on the agent. It selects the ones that apply to the task in front of it, personalized per prompt: a logging change pulls a different set than a data migration would.
Scale AI across the bank
without scaring Risk.
The mandate is to turn executive AI ambition into measurable engineering output, while giving risk, security, architecture, and audit enough confidence that the program won't blow back on them.
DevGrid is the control point: every agent grounded in real system state and your actual policies, every action traceable, every review in your existing path.
Two people carry the risk.
One context layer covers them both.
“The board wants AI everywhere by Q3. Risk wants proof it won't end up in a supervisory finding. I'm the one standing between them.”
A single place to distribute the right instructions (your standards, policies, and live system state) to every agent, with a governance story Risk will sign off on.
“Every team wants a different agent wired to a different tool. I can't hand-plumb context to all of them and still keep the paths golden.”
One context layer wired once into the golden paths, instead of plumbing every tool to every agent.
Bring your own agents.
DevGrid is the context layer, not another copilot.
scoped to the task