Engram Context, Routing & Safety

Control plane for AI-assisted engineering

Context, routing & safety for coding agents

Engram decides what agents need to know, who should run the work, what must be verified, and when a human must approve—before impact.

Try the demo Product Vision
Context routing Risk routing Evidence-first Human gates

Service Dependency Graph

Auth Service · Medium risk

Web App API Gateway Auth Service Email Service Redis Cache User Service

Related Incidents

  • INC-521 · Auth failures on login Resolved
  • INC-480 · Token refresh timeout Investigating
  • INC-307 · Elevated error rate Resolved

Linked PRs

  • PR-123 · Cache invalidation update Review required
  • PR-116 · Session expiry guardrails Merged

Decisions (ADR)

  • ADR-12 · Redis TTL strategy Accepted
  • ADR-09 · Auth ownership model Accepted

Risk Summary

Risk: Medium

  • 2 recent incidents in this path
  • High-impact downstream dependency
  • Human approval required

Not another coding agent—the control plane around them

Context routing Capability routing Risk routing Verification Governance

How it works

How it works in 3 steps

Step 1

Understand the task

An engineer or coding agent proposes work. Engram maps services, dependencies, and blast radius before anyone edits blindly.

Step 2

Route context & capability

Task-specific context from PRs, incidents, ADRs, and ownership—plus the minimum agent organization justified by risk (not a default agent swarm).

Step 3

Verify, then gate

Independent checks and policy outcomes—allow, review, or block—so impactful changes stay evidence-linked and human-governed when needed.

Scenarios

Where Engram helps most

On-call engineer

During incidents, quickly surface related changes, similar outages, owners, and likely impact paths.

PR reviewer / tech lead

Before merge, see historical risk context and dependency impact to make safer approval decisions.

Platform / DevEx team

Apply policy-aware guardrails so AI-assisted changes follow review and escalation expectations.

New engineer onboarding

Understand service relationships and prior incidents faster without digging through scattered tools.

Product Vision

The context, routing & safety layer around coding agents

Built now (alpha)

Context engine + adaptive router + thin agent org (Manager / Backend worktree / Reviewer) + deterministic risk gates + outcome log with similar-task lookup. Local try UI: BYO GitHub ingest/query/preflight, plus sample Auth risk loop.

Still vision

Trained routing policies from production outcomes, merge/deploy automation, CI governance inbox, and dogfood on a full product org—without shipping an agent swarm on day one.

Product

What Engram decides for every engineering task

01 — Context

Task-specific context

Pull only the incidents, PRs, ADRs, ownership, and dependency paths that matter for this change—not the same RAG blob for every request.

02 — Routing

Capability & risk routing

Match work to the right agent/model/tools and verification depth. Trivial docs stay cheap; auth and migrations get specialists and review.

03 — Verification

Independent checks

Implementers do not grade their own homework. Reviewers, tests, and policy run with separation of duties before a gate fires.

04 — Governance

Allow / review / block

Explicit outcomes with provenance. High-blast-radius work stays human-approved; Engram advises—it does not pretend certainty.

Architecture

Three graphs. Three routers. One control plane.

Context Graph (system reality)
Task Graph (intended work)
Agent / Capability Graph
Context · Capability · Risk Routing
Verification + Provenance
Governance Gate
Learning from Outcomes

/preflight · /query

Evidence-backed context packet and grounded answers

/run

Thin agent org + risk gate on a git worktree (sample Auth demo)

/outcomes

Human resolve + similar-task priors (log, not a trained policy)

Why It Wins

Defensible where generic coding assistants stop

Routing, not RAG theater

Task-specific context and risk paths—not the same retrieval recipe every time.

Traceable trust

Risk signals stay source-linked to incidents, PRs, ADRs, and ownership.

Outcome memory

Resolved runs become constraints on similar tasks today. Trained policy improvement from production outcomes is the later learning layer.

Evidence Panel

Source-linked recommendations, never black-box claims

Risk: Medium-high

Reasoning: Similar failure in INC-45, cache constraints in ADR-12, and recent invalidation update in PR-123.

Relationship path: PR-123 -> Auth Service -> INC-45

Confidence: Moderate (manual verification advised)

Manual check: Validate TTL parity before merge.

Policy Outcomes

Action decisions mapped to explicit risk conditions

Allow

Low-risk change, no recent incident links, owner verified.

Warn

Moderate uncertainty or incomplete context; proceed with caution.

Review

High-impact service or recent incident history requires owner review.

Block

Destructive or production-sensitive action without required approvals.

Trust Boundaries

Built to reduce risk, not automate blindly

Build Sequence

Context engine first—then routers, then learning

V1–V1.5

Context engine + adaptive router + evals — alpha

V2–V2.5

Thin agents, worktrees, deterministic risk gates — alpha

V3

Outcome log + similar-task lookup now; learned routing policies later

Build With Us

Start with the context engine. Grow into the control plane.

Live demo: public GitHub ingest, query/preflight, and the sample Auth risk loop on the hosted API. Trained routing and merge automation stay vision.

Try the demo

Contact

Connect with Engram leadership

Surya Nediyadeth

CEO

For investors and design partners evaluating Engram.